Methods of disease activity profiling for personalized therapy management

ABSTRACT

The present invention provides methods for personalized therapeutic management of a disease in order to optimize therapy and/or monitor therapeutic efficacy. In particular, the present invention comprises measuring an array of one or a plurality of biomarkers at a plurality of time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment. In certain instances, the therapeutic agent is a TNFα inhibitor for the treatment of a TNFα-mediated disease or disorder.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of Application No. PCT/US2012/037375,filed May 10, 2012, which application claims priority to U.S.Provisional Patent Application No. 61/484,607, filed May 10, 2011, U.S.Provisional Patent Application No. 61/505,026, filed Jul. 6, 2011, U.S.Provisional Application No. 61/553,909, filed Oct. 31, 2011, U.S.Provisional Application No. 61/566,509, filed Dec. 2, 2011, and U.S.Provisional Application No. 61/636,575, filed Apr. 20, 2012, thedisclosures of which are hereby incorporated by reference in theirentirety for all purposes.

BACKGROUND OF THE INVENTION

Inflammatory bowel disease (IBD) which includes Crohn's disease (CD) andulverative colitis (UC) is a chronic idiopathic inflammatory disorderaffecting the gatrointestine tract. Disease progression of CD and UCincludes repeated episodes of inflammation and ulceration of theintestine, leading to complications requiring hospitalization, surgeryand escalation of therapy (Peyrin-Biroulet et al., Am. J. Gastroenterol,105: 289-297 (2010); Langholz E., Dan. Med. Bull., 46: 400-415 (1999)).Current treatments such as anti-tumor necrosis factor-alpha (TNF-α)biologics (e.g., infliximab (IFX), etanercept, adalimumab (ADL) andcertolizumab pegol), thiopurine drugs (e.g., azathioprine (AZA),6-mercaptopurin (6-MP)), anti-inflammatory drugs (e.g., mesalazine), andsteroids (e.g., corticosteroids) have been shown to reduce diseaseactivity. In some clinical trials of CD, mucosal healing which isdescribed as the absence of intestinal ulcers, was induced in patientson combination therapy of corticosteroids, IFX and ADL. Furthermore, MHwas maintained in patients receiving IFX.

Other studies have shown that mucosal healing can be a hallmark ofsuppression of bowel inflammation and predict long-term diseaseremission (Froslie et al., Gastroenterology, 133: 412-422 (2007); Baertet al., Gastroenterology, (2010)). Long-term mucosal healing has beenassociated with a decreased risk of colectomy and colorectal cancer inUC patients, a decreased need for corticosteroid treatment in CDpatients, and possibly a decreased need for hospitalization (Dave etal., Gastroenterology & Hepatology, 8(1): 29-38 (2012)).

The International Organization for the Study of Inflammatory BowelDisease proposed defining mucosal healing in UC as the absence offriability, blood, erosions an dulcers in all visualized segments of gutmucosa (D'Haens et al., Gastroenterology, 132: 763-786 (2007)). MH in CDwas proposed to be the absence of ulcers. The gold standard formeasurement of Crohn's disease activity is the Crohn's DiseaseEndoscopic Index of Severity (CDEIS). This disease index score isestablished from several variables such as superficial and deepulceration, ulcerated and nonulcerated stenosis, and surface area ofulcerated and disease segments. A simplified version of the index is theSimple Endoscopic Score for Crohn's Disease, which takes into accountdisease variables including ulcer size, ulcerated surface, affectedsurface and presence of narrowing. Both indices evaluate clinicalsymptoms of CD, yet fail to measure the underlying cause of disease(e.g., inflammation) or resolution of disease (e.g., mucosal healing). Ameasurement of mucosal healing can be performed to assess diseaseinduction as well as disease progression and resolution.

The process of mucosal healing begins with bleeding (e.g., degradationof the endothelial layers of the blood vessels) and inflammation, thenprogresses to cell and tissue proliferation, and finally tissueremodeling. At the inflammation stage, inflammatory markers andanti-inflammatory markers, such as, but not limited to, IL-1, IL-2,IL-6, IL-14, IL-17, TGFβ, and TNFα are expressed. During remodeling,tissue repair and remodeling growth factors, such as, but not limitedto, AREG, EREG, HB-EGF, HGF, NRG1-4, BTC, EGF, IGF, TGF-α, VEGFs, FGFs,and TWEAK are expressed. Repair of the intestinal epithelium requiresmultiple signal transduction pathways which are necessary for cellsurvival, proliferation, and migration. We have identified novel markersof mucosal healing that are predictive of the risk of disease relapseand disease remission. A measurement of mucosal healing can be used toperiodically assess disease status in patients receiving a therapyregimen.

Mucosal healing is typically assessed by endoscopy. Although theinvasive procedure is considered to be low-risk, its cost and patientdiscomfort and compliance remain obstacles to frequent, regularendoscopies to assess mucosal healing. There is an unmet need in the artfor non-invasive methods of determining mucosal healing in a patient.

There is a need in the art for methods of therapeutic management ofdiseases such as autoimmune disorders using an individualized approachto optimize therapy and monitor efficacy. The methods need to includeassessing disease course and clinical parameters such asphamacokinetics, disease activity indices, disease burden, and mucosalstatus. The present invention satisfies this need and provides relatedadvantages as well.

BRIEF SUMMARY OF THE INVENTION

The present invention provides methods for personalized therapeuticmanagement of a disease in order to optimize therapy and/or monitortherapeutic efficacy. In particular, the present invention comprisesmeasuring an array of one or a plurality of mucosal healing biomarkersat one or a plurality of time points over the course of therapy with atherapeutic agent to determine a mucosal healing index for selectingtherapy, optimizing therapy, reducing toxicity, and/or monitoring theefficacy of therapeutic treatment. In some embodiments, the therapy isan anti-TNF therapy, an immunosuppressive agent, a corticosteroid, adrug that targets a different mechanism, a nutrition therapy andcombinations thereof. In certain instances, the anti-TNF therapy is aTNF inhibitor (e.g., anti-TNF drug, anti-TNFα antibody) for thetreatment of a TNFα-mediated disease or disorder.

TNFα has been implicated in inflammatory diseases, autoimmune diseases,viral, bacterial and parasitic infections, malignancies, and/orneurodegenerative diseases and is a useful target for specificbiological therapy in diseases, such as rheumatoid arthritis and Crohn'sdisease. TNF inhibitors such as anti-TNFα antibodies are an importantclass of therapeutics. In some embodiments, the methods of the presentinvention advantageously improve therapeutic management of patients witha TNFα-mediated disease or disorder by optimizing therapy and/ormonitoring therapeutic efficacy to anti-TNF drugs such as anti-TNFαtherapeutic antibodies.

As such, in one aspect, the present invention provides a non-invasivemethod for measuring mucosal healing in an individual diagnosed withinflammatory bowel disease (IBD) receiving a therapy regimen, the methodcomprising:

-   -   (a) measuring the levels of an array of mucosal healing markers        in a sample from the individual;    -   (b) comparing the levels of an array of mucosal healing markers        in the individual to that of a control to compute the mucosal        healing index of the individual, wherein the mucosal healing        index comprises a representation of the extent of mucosal        healing; and    -   (c) determining whether the individual undergoing mucosal        healing should maintain the therapy regimen.

As such, in one aspect, the present invention provides a method formonitoring therapeutic efficiency in an individual with IBD receivingtherapy, the method comprising:

-   -   (a) measuring levels of an array of mucosal healing markers in a        sample from the individual at a plurality of time points over        the course of therapy with a therapeutic antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the individual's mucosal healing index to that of        a control; and    -   (d) determining whether the therapy is appropriate for the        individual to promote mucosal healing.

In another aspect, the present invention provides a method for selectinga therapy regimen in an individual with IBD, the method comprising:

-   -   (a) measuring levels of an array of mucosal healing markers in a        sample from the individual at a plurality of time points over        the course of therapy, the individual receiving a therapeutic        antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the individual's mucosal healing index to that of        a control; and    -   (d) selecting an appropriate therapy regimen for the individual        wherein the therapy regimen promotes mucosal healing

As such, in another aspect, the present invention provides a method forreducing or minimizing the risk of surgery in an individual diagnosedwith IBD being administered a therapy regimen, the method comprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) generating the individual's mucosal healing index comprising        a representation of the presence and/or concentration levels of        each of the markers over time;    -   (c) comparing the individual's mucosal healing index to that of        a control, and    -   (d) selecting an appropriate therapy regimen for to reduce or        minimize the risk of surgery.

As such, in another aspect, the present invention provides a method forselecting a therapy regimen to promote mucosal healing in an individualdiagnosed with IBD, the method comprising:

-   -   (a) measuring levels of a panel of mucosal healing markers at        time point t₀ to generate a mucosal healing index at t₀;    -   (b) measuring levels of a panel of mucosal healing markers at        time point t₁ to generate a mucosal healing index at t₁;    -   (c) comparing the change in the mucosal healing index from t₀ to        t₁; and    -   (d) selecting the therapy regimen for the individual to promote        mucosal healing.

As such, in one aspect, the present invention provides a non-invasivemethod for measuring mucosal healing in an individual diagnosed withCrohn's disease receiving an anti-TNF therapy regimen, the methodcomprising:

-   -   (a) measuring the levels of an array of mucosal healing markers        in a sample from the individual;    -   (b) comparing the levels of an array of mucosal healing markers        in the individual to that of a control to compute the mucosal        healing index of the individual, wherein the mucosal healing        index comprises a representation of the extent of mucosal        healing; and    -   (c) determining whether the individual undergoing mucosal        healing should maintain the anti-TNF therapy regimen.

As such, in another aspect, the present invention provides a method formonitoring therapeutic efficiency in an individual with Crohn's diseasereceiving anti-TNF therapy, the method comprising:

-   -   (a) measuring levels of an array of mucosal healing markers in a        sample from the individual at a plurality of time points over        the course of therapy with a therapeutic antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the individual's mucosal healing index to that of        a control; and    -   (d) determining whether the anti-TNF therapy is appropriate for        the individual to promote mucosal healing.

As such, in another aspect, the present invention provides a method forselecting an anti-TNF therapy regimen in an individual with Crohn'sdisease, the method comprising:

-   -   (a) measuring levels of an array of mucosal healing markers in a        sample from the individual at a plurality of time points over        the course of therapy, the individual receiving a therapeutic        antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the individual's mucosal healing index to that of        a control; and    -   (d) selecting an appropriate anti-TNF therapy regimen for the        individual wherein the anti-TNF therapy promotes mucosal        healing.

As such, in another aspect, the present invention provides a method forreducing or minimizing the risk of surgery in an individual diagnosedwith Crohn's disease being administered an anti-TNF antibody therapyregimen, the method comprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) generating the individual's mucosal healing index comprising        a representation of the presence and/or concentration levels of        each of the markers over time;    -   (c) comparing the individual's mucosal healing index to that of        a control, and    -   (d) selecting an appropriate anti-TNF antibody therapy regimen        for to reduce or minimize the risk of surgery.

As such, in another aspect, the present invention provides a method forselecting an anti-TNF antibody therapy regimen to promote mucosalhealing in an individual diagnosed with Crohn's disease, the methodcomprising:

-   -   (a) measuring levels of a panel of mucosal healing markers at        time point t₀ to generate a mucosal healing index at t₀;    -   (b) measuring levels of a panel of mucosal healing markers at        time point t₁ to generate a mucosal healing index at t₁;    -   (c) comparing the change in the mucosal healing index from t₀ to        t₁; and    -   (d) selecting the anti-TNF antibody therapy regimen for the        individual to promote mucosal healing.

In some embodiments, the disease is a gastrointestinal disease or anautoimmune disease. In certain instances, the subject has Crohn'sdisease (CD) or rheumatoid arthritis (RA). In other embodiments, thetherapeutic antibody is an anti-TNFα antibody. In some embodiments, theanti-TNFα antibody is a member selected from the group consisting ofREMICADE™ (infliximab), ENBREL™ (etanercept), HUMIRA™ (adalimumab),CIMZIA® (certolizumab pegol), and combinations thereof. In preferredembodiments, the subject is a human.

In some embodiments, the array of markers comprises a mucosal healingmarker. In some embodiments, the mucosal marker comprises AREG, EREG,HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-α, VEGF-A,VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, TWEAK and combinationsthereof.

On other embodiments, the array of markers further comprises a memberselected from the group consisting of an anti-TNFα antibody, ananti-drug antibody (ADA), an inflammatory marker, an anti-inflammatorymarker, a tissue repair marker (e.g., a growth factor), and combinationsthereof. In certain instances, the anti-TNFα antibody is a memberselected from the group consisting of REMICADE™ (infliximab), ENBREL™(etanercept), HUMIRA™ (adalimumab), CIMZIA® (certolizumab pegol), andcombinations thereof. In certain other instances, the anti-drug antibody(ADA) is a member selected from the group consisting of a humananti-chimeric antibody (HACA), a human anti-humanized antibody (HAHA), ahuman anti-mouse antibody (HAMA), and combinations thereof. In yet otherinstances, the inflammatory marker is a member selected from the groupconsisting of GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, sTNF RII,and combinations thereof. In further instances, the anti-inflammatorymarker is a member selected from the group consisting of IL-12p70,IL-10, and combinations thereof.

In certain embodiments, the array comprises at least 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,30, 35, 40, 45, 50, or more markers. In some embodiments, the markersare measured in a biological sample selected from the group consistingof serum, plasma, whole blood, stool, peripheral blood mononuclear cells(PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy (e.g., from asite of inflammation such as a portion of the gastrointestinal tract orsynovial tissue).

In certain embodiments, the plurality of time points comprises at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25,30, 35, 40, 45, 50, or more time points. In some instances, the firsttime point in the plurality of time points is prior to the course oftherapy with the therapeutic antibody. In other instances, the firsttime point in the plurality of time points is during the course oftherapy with the therapeutic antibody. As non-limiting examples, each ofthe markers can be measured prior to therapy with a therapeutic antibodyand/or during the course of therapy at one or more (e.g., a plurality)of the following weeks: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44,46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100, etc.

In some embodiments, selecting an appropriate therapy comprisesmaintaining, increasing, or decreasing a subsequent dose of the courseof therapy for the subject. In other embodiments, the method furthercomprises determining a different course of therapy for the subject. Incertain instances, the different course of therapy comprises treatmentwith a different anti-TNFα antibody. In other instances, the differentcourse of therapy comprises the current course of therapy along withanother therapeutic agent, such as, but not limited to an anti-TNFtherapy, an immunosuppressive agent, a corticosteroid, a drug thattargets a different mechanism, a nutrition therapy and other combinationtreatments.

In some embodiments, selecting an appropriate therapy comprisesselecting an appropriate therapy for initial treatment. In someinstances, the therapy comprises an anti-TNFα antibody therapy.

In certain embodiments, the methods disclosed herein can be used asconfirmation that a proposed new drug or therapeutic is the same as oris sufficiently similar to an approved drug product, such that theproposed new drug can be used as a “biosimilar” therapeutic. Forexample, if the proposed new drug has only a slightly different diseaseactivity profile compared to the branded drug product, this would beapparent using the methods disclosed herein. If the proposed new drughas a significantly different disease activity profile compared to thebranded drug product, then the new drug would not be biosimilar.Advantageously, the methods disclosed herein can be used in clinicaltrials of proposed new drugs in order to assess the effectivetherapeutic efficacy or value of the drug.

Accordingly, in some aspects, the methods of the invention provideinformation useful for guiding treatment decisions for patientsreceiving or about to receive anti-TNF drug therapy, e.g., by selectingan appropriate anti-TNF therapy for initial treatment, by determiningwhen or how to adjust or modify (e.g., increase or decrease) thesubsequent dose of an anti-TNF drug, by determining when or how tocombine an anti-TNF drug (e.g., at an initial, increased, decreased, orsame dose) with one or more immunosuppressive agents such asmethotrexate (MTX) or azathioprine (AZA), and/or by determining when orhow to change the current course of therapy (e.g., switch to a differentanti-TNF drug or to a drug that targets a different mechanism such as anIL-6 receptor-inhibiting monoclonal antibody, anti-integrin molecule(e.g., Tysabri, Vedaluzamab), JAK-2 inhibitor, and tyrosine kinaseinhibitor, or to a nutritition therapy (e.g., special carbohydratediet)).

In other embodiments, the methods of the present invention can be usedto predict responsiveness to a TNFα inhibitor, especially to ananti-TNFα antibody in a subject having an autoimmune disorder (e.g.,rheumatoid arthritis, Crohn's Disease, ulcerative colitis and thelike.). In this method, by assaying the subject for the correct ortherapeutic dose of anti-TNFα antibody, i.e., the therapeuticconcentration level, it is possible to predict whether the individualwill be responsive to the therapy.

In another embodiment, the present invention provides methods formonitoring IBD (e.g., Crohn's disease and ulcerative colitis) in asubject having the IBD disorder, wherein the method comprises assayingthe subject for the correct or therapeutic dose of anti-TNFα antibody,i.e., the therapeutic concentration level, over time. In this manner, itis possible to predict whether the individual will be responsive to thetherapy over the given time period.

Other objects, features, and advantages of the present invention will beapparent to one of skill in the art from the following detaileddescription and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a personalized IBD activity profile as described in Example1.

FIG. 2A show various patient infliximab concentrations as a function oftreatment time. FIG. 2B shows patient ranks over a course of treatmentwith events (infliximab falling below a threshold concentration) noted.

FIG. 3A show various patient HACA (ATI) concentrations as a function oftreatment time. FIG. 3B shows patient ranks over a course of treatmentwith events (HACA detection or appearance) noted.

FIG. 4A illustrates an association between the presence of ATI and thelevel of IFX in patient samples. Samples with no detectable level of ATIhad a significantly higher IFX median concentration, compared to samplewith detectable ATI. FIG. 4B illustrates that the presence of ATIcorrelates with higher CDAI. FIG. 4C shows that concurrentimmunosuppressant therapy (e.g., MTX) is more likely to suppress thepresence of ATI.

FIG. 5A shows that patients with ATI are more likely to develop a poorresponse to treatment. FIG. 5B illustrates that the inflammatory markerCRP is associated with increased levels of ATI.

FIG. 6 illustrates that the protein levels of an array of one or moreinflammatory and tissue repair markers correlate to the formation ofantibodies to IFX.

FIG. 7A illustrates that an array of inflammatory markers can be used toestablish an inflammatory index what correlates with the presence of ATIand/or disease progression.

FIG. 7B shows the relationship between the PII and IFX concentrations insamples with ATI present. FIG. 7C illustrates that an exemplary PROInflammatory Index correlates with levels of IFX (p<0.0001 andR²=−0.129) in patient samples of the COMMIT study.

FIG. 8A illustrates the correlation between Crohn's Disease ActivityIndex (CDAI) score and the concentration of infliximab in serum in anumber of patients in clinical study #1. FIG. 8B shows that the presenceof IFX in a sample correlated with a higher CDAI.

FIG. 9A illustrates the association between IFX concentration and thepresence of antidrug antibodies to inflixamab in samples analyzed. FIG.9B illustrates that a high concentration of ATI can lead to neutralizingantibodies and undetectable levels of IFX. FIG. 9C illustrates that anATI positive sample determined at an early time point leads to a higherCDAI at a later time point, compared to the lower CDAI level from an ATInegative sample. “V1”=Visit 1; “V3”=Visit 3. FIG. 9D illustrates that inclinical study #1, patients had lower odds of developing ATI ifreceiving a combination therapy of infliximab and an immunosuppressantagent (e.g., MTX and AZA).

FIG. 10A shows that correlation between IFX concentration and thepresence of ATI in samples of clinical study #2A. FIG. 10B illustratesthe relationship between ISA therapy and the presence of ATI in thestudy. FIG. 10C illustrates the relationship between CRP concentrationsand the presence of ATI (ATI and/or neutralizing ATI). FIG. 10Dillustrates the relationship between loss of responsiveness to IFXtherapy and the presence of ATI in the study.

FIG. 11 illustrates that levels of ATI and neutralizing antibodies canbe determined over time in a series of samples from various patients.

FIG. 12A illustrates the comparison of CRP levels to the presence ofIFX. FIG. 12B illustrates the relationship between the presence of ATIand the infusion reaction. FIG. 12C illustrates the relationship betweenIFX concentration and the presence of ATI in clinical study #2B. FIG.12D illustrates the correlation between the presence of ATI and thewithdrawal of ISA therapy at a specific, given date.

FIG. 13A illustrates the relationship between ATI and the inflammatorymarker CRP. Our analysis showed that the odds of experiencing a loss ofresponse to IFX was higher in patients determined to be ATI positive atany time point. FIG. 13B illustrates the correlation between thepresence of ATI at any time point and responsiveness to IFX treatment.FIG. 13C shows that loss of response can be related to an increase inCRP. FIG. 13D illustrates the association between the presence of IFXand CRP levels.

FIG. 14A shows that lower IFX levels are associated with the presence ofATI in clinical study #2C. FIG. 14B shows that lower IFX levels areassociated with the presence of ATI in clinical study #3. FIG. 14Cillustrates that the same correlation between IFX levels and ATI wasalso present in the study data, follow-up study and in thepharmacokinetics study.

FIG. 15A illustrates the relationship between ATI levels and IFX. It wasdetermined that samples with high concentration ATI are neutralizing onIFX and thus, IFX concentration was determined to be 0 μg/ml. FIG. 15Billustrates an association between ADL concentration and the presence ofATA in patient samples.

FIG. 16A describes the details of an exemplary PRO Inflammatory Index.FIG. 16B illustrates that there is no obvious relationship between thePII and the concentration of ADL in an array of samples with ADL aloneor in combination with other drugs.

FIG. 17 shows a plot of the PII scores for patients receiving Humira andHumira in combination with other drug such as Remicade, Cimzia,Asathioprine and Methotrexate.

FIG. 18 shows details of methods for improved patient management of CDand/or UC.

FIG. 19 shows the effect of the TNF-α pathway and related pathways ondifferent cell types, cellular mechanisms and disease (e.g., Crohn'sDisease (CD), rheumatoid arthritis (RA) and Psoriasis (Ps)).

FIG. 20 illustrates an exemplary CEER multiplex growth factor array.

FIGS. 21A-G illustrate multiplexed growth factor profiling of patientsamples using CEER growth factor arrays.

FIG. 22 illustrates the association between CRP levels and the growthfactor index score in determining disease remission.

FIGS. 23A-C illustrate embodiments of the present invention to assist indeveloping personalized patient treatment for an IBD patient with mild,moderate, or severe disease activity.

FIG. 24 illustrates a treatment paradigm to personalize patienttreatment. Monitoring of disease burden and mucosal healing can assistin determining treatment selection, dose selection, and initial drugresponse.

FIG. 25 shows the ROC analysis of CRP and IFX trough thresholds.

FIGS. 26A-B show the relationship of CRP, serum IFX concentration andATI at sequential time points. FIG. 26A shows presence of IFX and ATI inthe pair's first data point and CRP in the subsequent measurements. FIG.26B shows CRP levels, IFX serum concentration and ATI status atsequential time points for a sample. In this sample CRP levels arelowest when the patient is ATI− and has a serum IFX concentration higherthan threshold.

FIG. 27 shows that there was no association between IFX levels higherthan threshold and CRP in ATI+ patients. Yet, in ATI− patients CRPlevels were significantly higher in patients with IFX levels less thanthreshold (3 μg/ml).

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present invention provides methods for measuring mucosal healing inpatients with IBD, CD and/or UC. In particular, the present inventionprovides methods of measuring mucosal healing markers wherein themarkers are indicative of intestinal tissue repair, and diseaseresolution or remission.

The present invention is advantageous because it addresses and overcomescurrent limitations associated with monitoring mucosal healing inpatients with IBD (e.g., Crohn's disease and ulcerative colitis). Thepresent invention provides non-invasive methods for monitoring mucosalhealing patients receiving anti-TNF therapy. In addition, the presentinvention provides methods of predicting therapeutic response, risk ofrelapse, and risk of surgery in patients with IBD (e.g., Crohn's diseaseand ulcerative colitis). In particular, the methods of the presentinvention find utility for selecting an appropriate anti-TNF therapy forinitial treatment, for determining when or how to adjust or modify(e.g., increase or decrease) the subsequent dose of an anti-TNF drug tooptimize therapeutic efficacy and/or to reduce toxicity, for determiningwhen or how to combine an anti-TNF drug (e.g., at an initial, increased,decreased, or same dose) with one or more immunosuppressive agents suchas methotrexate (MTX) or azathioprine (AZA), and/or for determining whenor how to change the current course of therapy (e.g., switch to adifferent anti-TNF drug or to a drug that targets a differentmechanism). The present invention also provides methods for selecting anappropriate therapy for patients diagnosed with CD, wherein the therapypromotes mucosal healing.

II. Definitions

As used herein, the following terms have the meanings ascribed to themunless specified otherwise.

The phrase “mucosal healing index” includes an empirically derived indexthat is based upon an analysis of a plurality of mucosal healingmarkers. In one aspect, the concentration of markers or their measuredconcentration values are transformed into an index by an algorithmresident on a computer. In certain aspects, the index is a synthetic orhuman derived output, score, or cut off value(s), which expresses thebiological data in numerical terms. The index can be used to determineor make or aid in making a clinical decision. A mucosal healing indexcan be measured multiple times over the course of time. In one aspect,the algorithm can be trained with known samples and thereafter validatedwith samples of known identity.

The phrase “mucosal healing index control” includes a mucosal healingindex derived from a healthy individual, or an individual who hasprogressed from a disease state to a healthy state. Alternatively, thecontrol can be an index representing a time course of a more diseasedstate to a less disease state or to a healthy state.

The phrase “determining the course of therapy” and the like includes theuse of an empirically derived index, score or analysis to select forexample, selecting a dose of drug, selecting an appropriate drug, or acourse or length of therapy, a therapy regimen, or maintenance of anexisting drug or dose. In certain aspects, a derived or measured indexcan be used to determine the course of therapy.

The terms “TNF inhibitor”, “TNF-α inhibitor” and “TNFα inhibitor” asused herein are intended to encompass agents including proteins,antibodies, antibody fragments, fusion proteins (e.g., Ig fusionproteins or Fc fusion proteins), multivalent binding proteins (e.g., DVDIg), small molecule TNF-α antagonists and similar naturally- ornormaturally-occurring molecules, and/or recombinant and/or engineeredforms thereof, that, directly or indirectly, inhibits TNF α activity,such as by inhibiting interaction of TNF-α with a cell surface receptorfor TNF-α, inhibiting TNF-α protein production, inhibiting TNF-α geneexpression, inhibiting TNFα secretion from cells, inhibiting TNF-αreceptor signaling or any other means resulting in decreased TNF-αactivity in a subject. The term “TNFα inhibitor” preferably includesagents which interfere with TNF-α activity. Examples of TNF-α inhibitorsinclude etanercept (ENBREL™, Amgen), infliximab (REMICADE™, Johnson andJohnson), human anti-TNF monoclonal antibody adalimumab (D2E7/HUMIRA™,Abbott Laboratories), CDP 571 (Celltech), and CDP 870 (Celltech), aswell as other compounds which inhibit TNF-α activity, such that whenadministered to a subject suffering from or at risk of suffering from adisorder in which TNF-α activity is detrimental (e.g., RA), the disorderis treated.

The term “predicting responsiveness to a TNFα inhibitor”, as usedherein, is intended to refer to an ability to assess the likelihood thattreatment of a subject with a TNF inhibitor will or will not beeffective in (e.g., provide a measurable benefit to) the subject. Inparticular, such an ability to assess the likelihood that treatment willor will not be effective typically is exercised after treatment hasbegun, and an indicator of effectiveness (e.g., an indicator ofmeasurable benefit) has been observed in the subject. Particularlypreferred TNFα inhibitors are biologic agents that have been approved bythe FDA for use in humans in the treatment of rheumatoid arthritis,which agents include adalimumab (HUMIRA™), infliximab (REMICADE™) andetanercept (ENBREL™), most preferably adalimumab (HUMIRA™).

The term “course of therapy” includes any therapeutic approach taken torelieve or prevent one or more symptoms associated with a TNαF-mediateddisease or disorder. The term encompasses administering any compound,drug, procedure, and/or regimen useful for improving the health of anindividual with a TNFα-mediated disease or disorder and includes any ofthe therapeutic agents described herein. One skilled in the art willappreciate that either the course of therapy or the dose of the currentcourse of therapy can be changed (e.g., increased or decreased) basedupon the presence or concentration level of TNF, anti-TNF drug, and/oranti-drug antibody using the methods of the present invention.

The term “immunosuppressive agent” includes any substance capable ofproducing an immunosuppressive effect, e.g., the prevention ordiminution of the immune response, as by irradiation or byadministration of drugs such as anti-metabolites, anti-lymphocyte sera,antibodies, etc. Examples of suitable immunosuppressive agents include,without limitation, thiopurine drugs such as azathioprine (AZA) andmetabolites thereof; anti-metabolites such as methotrexate (MTX);sirolimus (rapamycin); temsirolimus; everolimus; tacrolimus (FK-506);FK-778; anti-lymphocyte globulin antibodies, anti-thymocyte globulinantibodies, anti-CD3 antibodies, anti-CD4 antibodies, and antibody-toxinconjugates; cyclosporine; mycophenolate; mizoribine monophosphate;scoparone; glatiramer acetate; metabolites thereof; pharmaceuticallyacceptable salts thereof; derivatives thereof; prodrugs thereof; andcombinations thereof.

The term “thiopurine drug” includes azathioprine (AZA), 6-mercaptopurine(6-MP), or any metabolite thereof that has therapeutic efficacy andincludes, without limitation, 6-thioguanine (6-TG),6-methylmercaptopurine riboside, 6-thioinosine nucleotides (e.g.,6-thioinosine monophosphate, 6-thioinosine diphosphate, 6-thioinosinetriphosphate), 6-thioguanine nucleotides (e.g., 6-thioguanosinemonophosphate, 6-thioguanosine diphosphate, 6-thioguanosinetriphosphate), 6-thioxanthosine nucleotides (e.g., 6-thioxanthosinemonophosphate, 6-thioxanthosine diphosphate, 6-thioxanthosinetriphosphate), derivatives thereof, analogues thereof, and combinationsthereof.

The term “sample” as used herein includes any biological specimenobtained from a patient. Samples include, without limitation, wholeblood, plasma, serum, red blood cells, white blood cells (e.g.,peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN)cells), ductal lavage fluid, nipple aspirate, lymph (e.g., disseminatedtumor cells of the lymph node), bone marrow aspirate, saliva, urine,stool (i.e., feces), sputum, bronchial lavage fluid, tears, fine needleaspirate (e.g., harvested by random periareolar fine needle aspiration),any other bodily fluid, a tissue sample such as a biopsy of a site ofinflammation (e.g., needle biopsy), and cellular extracts thereof. Insome embodiments, the sample is whole blood or a fractional componentthereof such as plasma, serum, or a cell pellet. In other embodiments,the sample is obtained by isolating PBMCs and/or PMN cells using anytechnique known in the art. In yet other embodiments, the sample is atissue biopsy, e.g., from a site of inflammation such as a portion ofthe gastrointestinal tract or synovial tissue.

The term “Crohn's Disease Activity Index” or “CDAI” includes a researchtool used to quantify the symptoms of patients with Crohn's disease(CD). The CDAI is generally used to define response or remission of CD.The CDAI consists of eight factors, each summed after adjustment with aweighting factor. The components of the CDAI and weighting factors arethe following:

Weighting Clinical or laboratory variable factor Number of liquid orsoft stools each day for seven days ×2 Abdominal pain (graded from 0-3on severity) each day ×5 for seven days General well being, subjectivelyassessed from 0 (well) ×7 to 4 (terrible) each day for seven daysPresence of complications* ×20 Taking Lomitil or opiates for diarrhea×30 Presence of an abdominal mass (0 as none, 2 as ×10 questionable, 5as definite) Hematocrit of <0.47 in men and <0.42 in women ×6 Percentagedeviation from standard weight ×1One point each is added for each set of complications:

-   -   the presence of joint pains (arthralgia) or frank arthritis;    -   inflammation of the iris or uveitis;    -   presence of erythema nodosum, pyoderma gangrenosum, or aphthous        ulcers;    -   anal fissures, fistulae or abscesses;    -   other fistulae; and/or    -   fever during the previous week.

Remission of Crohn's disease is typically defined as a fall in the CDAIof less than 150 points. Severe disease is typically defined as a valueof greater than 450 points. In certain aspects, response to a particularmedication in a Crohn's disease patient is defined as a fall of the CDAIof greater than 70 points.

The terms “mucosal injury” or “mucosal damage” include the formation ofmacroscopically visible mucosal lesions in the intestines detectableduring endoscopy, granuloma formation and disruption of the muscularislayer at the microscopic tissue level, epithelial apoptosis andinfiltration of activated inflammatory and lymphocytic cells at thecellular level, increased epithelial permeability at a sub-cellularlevel, and gap junction disruption at a molecular level. In IBD such asCrohn's disease, the intestinal epithelium is damaged by theinflammatory environment, which results in the formation of refractoryulcers and lesions.

The term “mucosal healing” refers to restoration of normal mucosalappearance of a previously inflamed region, and complete absence ofulceration and inflammation at the endoscopic and microscopic levels.Mucosal healing includes repair and restoration of the mucosa,submucosa, and muscularis layers. It can also include neuronal andlymphangiogenic elements of the intestinal wall.

The term “nutrition-based therapy” includes butyrate, probiotics (e.g.,VSL#3, E. coli Nissle 1917, bacterium bacillus polyfermenticus),vitamins, proteins, macromolecules, and/or chemicals that promotemucosal healing such as growth and turnover of intestinal mucosa.

III. Description of the Embodiments

The present invention provides methods for personalized therapeuticmanagement of a disease in order to optimize therapy and/or monitortherapeutic efficacy. In particular, the present invention comprisesmeasuring an array of one or a plurality of mucosal healing biomarkersat one or a plurality of time points over the course of therapy with atherapeutic agent to determine a mucosal healing index for selectingtherapy, optimizing therapy, reducing toxicity, and/or monitoring theefficacy of therapeutic treatment. In certain instances, the therapeuticagent is a TNFα inhibitor for the treatment of a TNFα-mediated diseaseor disorder. In some embodiments, the methods of the present inventionadvantageously improve therapeutic management of patients with aTNFα-mediated disease or disorder by optimizing therapy and/ormonitoring therapeutic efficacy to anti-TNF drugs such as anti-TNFαtherapeutic antibodies.

As such, in one aspect, the present invention provides a method forpersonalized therapeutic management of a disease in order to optimizetherapy or monitor therapeutic efficacy in a subject, the methodcomprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) generating the subject's mucosal healing index comprising a        representation of the presence and/or concentration levels of        each of the markers over time;    -   (c) comparing the subject's mucosal healing index to that of a        control; and    -   (d) selecting an appropriate therapy for the subject, to thereby        achieve personalized therapeutic management of the disease in        the subject.

As such, in another aspect, the present invention provides a method forpersonalized therapeutic management of a disease in order to selecttherapy in a subject, the method comprising:

-   -   (a) measuring an array of mucosal healing markers;    -   (b) generating the subject's mucosal healing index comprising a        representation of the presence and/or concentration levels of        each of the markers;    -   (c) comparing the subject's mucosal healing index to that of a        control; and    -   (d) selecting an appropriate therapy for the subject, to thereby        achieve personalized therapeutic management of the disease in        the subject.

As such, in one aspect, the present invention provides a method foroptimizing therapy in a subject, the method comprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the subject's mucosal healing index to that of a        control; and    -   (d) determining a subsequence dose of the course of therapy for        the subject or whether a different course of therapy should be        administered to the subject based upon the mucosal healing        index.

As such, in one aspect, the present invention provides a method forselecting therapy in a subject, the method comprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the subject's mucosal healing index to that of a        control; and    -   (d) selecting an appropriate course of therapy for the subject        based upon the mucosal healing index.

As such, in another aspect, the present invention provides a method forreducing the risk of surgery in a subject diagnosed with IBD (e.g.,Crohn's disease) being administered a therapy regimen (e.g., an anti-TNFantibody therapy regimen), the method comprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the subject's mucosal healing index to that of a        control; and    -   (d) determining whether the therapy regimen is reducing the        subject's risk of surgery.

As such, in one aspect, the present invention provides a method formonitoring therapeutic efficiency in a subject receiving therapy (e.g.,anti-TNF therapy), the method comprising:

-   -   (a) measuring an array of mucosal healing markers at a plurality        of time points over the course of therapy with a therapeutic        antibody;    -   (b) applying a statistical algorithm to the level of the one or        more markers determined in step (a) to generate a mucosal        healing index;    -   (c) comparing the subject's mucosal healing index to that of a        control; and    -   (d) determining whether the current course of therapy is        appropriate for the subject based upon the mucosal healing        index.

In some embodiments, the disease is a gastrointestinal disease or anautoimmune disease. In certain instances, the subject has inflammatorybowel disease (IBD, e.g., Crohn's disease (CD) or ulcerative colitis(UC)). In other instances, the subject has rheumatoid arthritis (RA). Inpreferred embodiments, the subject is a human.

In some embodiments, the therapy is selected from the group comprisingan anti-TNF therapy, an immunosuppressive agent, a corticosteroid, adrug that targets a different mechanism, a nutrition therapy orcombinations thereof. In certain instances, the anti-TNF therapy is aTNF inhibitor (e.g., anti-TNF drug, anti-TNFα antibody).

In other embodiments, the anti-TNF therapy is an anti-TNFα antibody. Insome embodiments, the anti-TNFα antibody is a member selected from thegroup consisting of REMICADE™ (infliximab), ENBREL™ (etanercept),HUMIRA™ (adalimumab), CIMZIA® (certolizumab pegol), and combinationsthereof. In preferred embodiments, the subject is a human.

In some embodiments, the therapy is an immunosuppressive agent.Non-limiting examples of immunosuppressive agents include thiopurinedrugs such as azathioprine (AZA), 6-mercaptopurine (6-MP), and/or anymetabolite thereof that has therapeutic efficacy and includes, withoutlimitation, 6-thioguanine (6-TG), 6-methylmercaptopurine riboside,6-thioinosine nucleotides (e.g., 6-thioinosine monophosphate,6-thioinosine diphosphate, 6-thioinosine triphosphate), 6-thioguaninenucleotides (e.g., 6-thioguanosine monophosphate, 6-thioguanosinediphosphate, 6-thioguanosine triphosphate), 6-thioxanthosine nucleotides(e.g., 6-thioxanthosine monophosphate, 6-thioxanthosine diphosphate,6-thioxanthosine triphosphate), derivatives thereof, analogues thereof,and combinations thereof; anti-metabolites such as methotrexate (MTX);sirolimus (rapamycin); temsirolimus; everolimus; tacrolimus (FK-506);FK-778; anti-lymphocyte globulin antibodies, anti-thymocyte globulinantibodies, anti-CD3 antibodies, anti-CD4 antibodies, and antibody-toxinconjugates; cyclosporine; mycophenolate; mizoribine monophosphate;scoparone; glatiramer acetate; metabolites thereof; pharmaceuticallyacceptable salts thereof; derivatives thereof; prodrugs thereof; andcombinations thereof.

In other embodiments, the therapy is a corticosteroid. In yet otherembodiments, the therapy is a drug that targets a different mechanism(e.g., a mechanism that is not mediated by the TNFα pathway).Non-limiting examples of a drug that targets a different mechanisminclude IL-6 receptor inhibiting monoclonal antibodies, anti-integrinmolecules (e.g., natalizumab (Tysabri), vedoluzamab), JAK-2 inhibitors,tyrosine kinase inhibitors, and combinations thereof.

In other embodiments, the therapy is a nutrition therapy. In particularembodiments, the nutrition therapy is a special carbohydrate diet.Special carbohydrate diet (SCD) is a strict grain-free, lactose-free,and sucrose-free nutritional regimen that was designed to reduce thesymptoms of IBD such as Crohn's disease and ulcerative colitis. It hasbeen shown that SCD can promote and/or maintain mucosal healing inpatients with IBD (e.g., Crohn's disease or ulcerative colitis).Typically, SCD restricts the use of complex carbohydrates and eliminatesrefined sugar, grains and starch from the diet. It has been describedthat the microvilli of patients with IBD lack the ability to break downspecific types of complex carbohydrates, resulting in the overgrowth ofharmful bacteria and irritation of the gut mucosa. It has beenrecommended that SCD is a therapy for IBD (e.g., Crohn's disease orulcerative colitis) because it enables the gut to undergo mucosalhealing.

In some embodiments, the array of markers comprises a mucosal healingmarker. In some embodiments, the mucosal marker comprises AREG, EREG,HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-α, VEGF-A,VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, TWEAK and combinationsthereof.

In other embodiments, the array of markers further comprises a memberselected from the group consisting of an anti-TNFα antibody, ananti-drug antibody (ADA), an inflammatory marker, an anti-inflammatorymarker, a tissue repair marker (e.g., a growth factor), and combinationsthereof. In certain instances, the anti-TNFα antibody is a memberselected from the group consisting of REMICADE™ (infliximab), ENBREL™(etanercept), HUMIRA™ (adalimumab), CIMZIA® (certolizumab pegol), andcombinations thereof. In certain other instances, the anti-drug antibody(ADA) is a member selected from the group consisting of a humananti-chimeric antibody (HACA), a human anti-humanized antibody (HAHA), ahuman anti-mouse antibody (HAMA), and combinations thereof. In yet otherinstances, the inflammatory marker is a member selected from the groupconsisting of GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, sTNF RII,and combinations thereof. In further instances, the anti-inflammatorymarker is a member selected from the group consisting of IL-12p70,IL-10, and combinations thereof.

In certain embodiments, the array comprises at least 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,30, 35, 40, 45, 50, or more markers. In some embodiments, the markersare measured in a biological sample selected from the group consistingof serum, plasma, whole blood, stool, peripheral blood mononuclear cells(PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy (e.g., from asite of inflammation such as a portion of the gastrointestinal tract orsynovial tissue).

In certain embodiments, the plurality of time points comprises at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25,30, 35, 40, 45, 50, or more time points. In some instances, the firsttime point in the plurality of time points is prior to the course oftherapy with the therapeutic antibody. In other instances, the firsttime point in the plurality of time points is during the course oftherapy with the therapeutic antibody. As non-limiting examples, each ofthe markers can be measured prior to therapy with a therapeutic antibodyand/or during the course of therapy at one or more (e.g., a plurality)of the following weeks: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44,46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100, etc.

In further embodiments, the method for assessing or measuring mucosalhealing further comprises comparing the determined level of the mucosalhealing marker present in a sample to an index value or cutoff value orreference value or threshold value, wherein the level of the mucosalhealing marker above or below that value is predictive or indicative ofan increased or higher likelihood of the subject either undergoingmucosal healing or not undergoing mucosal healing. One skilled in theart will understand that the index value or cutoff value or referencevalue or threshold value is in units such as mg/ml, μg/ml, ng/ml, pg/ml,fg/ml, EU/ml, or U/ml depending on the marker of interest that is beingmeasured.

In some embodiments, the mucosal healing index includes an empiricallyderived index that is based upon an analysis of a plurality of mucosalhealing markers. In one aspect, the concentration of markers or theirmeasured concentration values are transformed into an index by analgorithm resident on a computer. In certain aspects, the index is asynthetic or human derived output, score, or cut off value(s), whichexpresses the biological data in numerical terms. The index can be usedto determine or make or aid in making a clinical decision. A mucosalhealing index can be measured multiple times over the course of time. Inone aspect, the algorithm can be trained with known samples andthereafter validated with samples of known identity.

In some embodiments, the mucosal healing index control is a mucosalhealing index derived from a healthy individual, or an individual whohas progressed from a disease state to a healthy state. Alternatively,the control can be an index representing a time course of a morediseased state or healthy to disease.

In some embodiments, the methods of determining the course of therapyand the like include the use of an empirically derived index, score oranalysis to select for example, selecting a dose of drug, selecting anappropriate drug, or a course or length of therapy, a therapy regimen,or maintenance of an existing drug or dose. In certain aspects, aderived or measured index can be used to determine the course oftherapy.

In some embodiments, mucosal healing can be assessed or monitored byendoscopy. Non-limiting examples of endoscopy include video capsuleendoscopy (capsule endoscopy), disposable endoscopy, and 3D endoscopy.In other embodiment, the mucosal healing index is monitored or confirmedby endoscopy.

In some embodiments, selecting an appropriate therapy comprisesmaintaining, increasing, or decreasing a subsequent dose of the courseof therapy for the subject. In other embodiments, the method furthercomprises determining a different course of therapy for the subject. Incertain instances, the different course of therapy comprises treatmentwith a different anti-TNFα antibody. In other instances, the differentcourse of therapy comprises the current course of therapy along withanother therapeutic agent, such as, but not limited to, animmunosuppressive agent, a corticosteroid, a drug that targets adifferent mechanism, nutrition therapy, and combinations thereof).

In some embodiments, selecting an appropriate therapy comprisesselecting an appropriate therapy for initial treatment. In someinstances, the therapy comprises an anti-TNFα antibody therapy.

In certain embodiments, the methods disclosed herein can be used asconfirmation that a proposed new drug or therapeutic is the same as oris sufficiently similar to an approved drug product, such that theproposed new drug can be used as a “biosimilar” therapeutic. Forexample, if the proposed new drug has only a slightly different diseaseactivity profile compared to the branded drug product, this would beapparent using the methods disclosed herein. If the proposed new drughas a significantly different disease activity profile compared to thebranded drug product, then the new drug would not be biosimilar.Advantageously, the methods disclosed herein can be used in clinicaltrials of proposed new drugs in order to assess the effectivetherapeutic value of the drug.

Accordingly, in some aspects, the methods of the invention provideinformation useful for guiding treatment decisions for patientsreceiving or about to receive anti-TNF drug therapy, e.g., by selectingan appropriate anti-TNF therapy for initial treatment, by determiningwhen or how to adjust or modify (e.g., increase or decrease) thesubsequent dose of an anti-TNF drug, by determining when or how tocombine an anti-TNF drug (e.g., at an initial, increased, decreased, orsame dose) with one or more immunosuppressive agents such asmethotrexate (MTX) or azathioprine (AZA), and/or by determining when orhow to change the current course of therapy (e.g., switch to a differentanti-TNF drug or to a drug that targets a different mechanism such as anIL-6 receptor-inhibiting monoclonal antibody, anti-integrin molecule(e.g., Tysabri, Vedaluzamab), JAK-2 inhibitor, and tyrosine kinaseinhibitor, or to a nutritition therapy (e.g., special carbohydratediet)).

In other embodiments, the methods of the present invention can be usedto predict responsiveness to a TNFα inhibitor, especially to ananti-TNFα antibody in a subject having an autoimmune disorder (e.g.,rheumatoid arthritis, Crohn's Disease, ulcerative colitis and thelike.). In this method, by assaying the subject for the correct ortherapeutic dose of anti-TNFα antibody, i.e., the therapeuticconcentration level, it is possible to predict whether the individualwill be responsive to the therapy.

In another embodiment, the present invention provides methods formonitoring IBD (e.g., Crohn's disease and ulcerative colitis) in asubject having the IBD disorder, wherein the method comprises assayingthe subject for the correct or therapeutic dose of anti-TNFα antibody,i.e., the therapeutic concentration level, over time. In this manner, itis possible to predict whether the individual will be responsive to thetherapy over the given time period.

In certain embodiments, step (a) comprises determining the presenceand/or level of at least two, three, four, five, six, seven, eight,nine, ten, fifteen, twenty, thirty, forty, fifty, or more markers in thesample.

In other embodiments, the algorithm comprises a learning statisticalclassifier system. In some instances, the learning statisticalclassifier system is selected from the group consisting of a randomforest, classification and regression tree, boosted tree, neuralnetwork, support vector machine, general chi-squared automaticinteraction detector model, interactive tree, multiadaptive regressionspline, machine learning classifier, and combinations thereof. Incertain instances, the statistical algorithm comprises a single learningstatistical classifier system. In certain other instances, thestatistical algorithm comprises a combination of at least two learningstatistical classifier systems. In some instances, the at least twolearning statistical classifier systems are applied in tandem.Non-limiting examples of statistical algorithms and analysis suitablefor use in the invention are described in International Application No.PCT/US2011/056777, filed Oct. 18, 2011, the disclosure of which ishereby incorporated by reference in its entirety for all purposes.

In other embodiments, step (b) further comprises applying a statisticalalgorithm to the presence and/or level of one or more mucosal healingmarkers determined at an earlier time during the course of therapy togenerate an earlier mucosal healing index. In some instances, theearlier mucosal healing index is compared to the mucosal healing indexgenerated in step (b) to determine a subsequent dose of the course oftherapy or whether a different course of therapy should be administered.In certain embodiments, the subsequent dose of the course of therapy isincreased, decreased, or maintained based upon mucosal healing indexgenerated in step (b). In some instances, the different course oftherapy comprises a different anti-TNFα antibody. In other instances,the different course of therapy comprises the current course of therapyalong with an immunosuppressive agent.

In some embodiments, step (b) further comprises applying a statisticalalgorithm to the presence and/or level of one or more of the mucosalhealing markers determined at an earlier time to generate an earlierdisease activity/severity index. In certain instances, the mucosalhealing index is compared to the mucosal healing index generated in step(b) to predict the course of the TNF-mediated disease or disorder.

In some embodiments, the method further comprises sending the resultsfrom the selection or determination of step (d) to a clinician. In otherembodiments, step (d) comprises selecting an initial course of therapyfor the subject.

Once the diagnosis or prognosis of a subject receiving anti-TNF drugtherapy has been determined or the likelihood of response to an anti-TNFdrug has been predicted in a subject diagnosed with a disease anddisorder in which TNF has been implicated in the pathophysiology, e.g.,but not limited to, shock, sepsis, infections, autoimmune diseases, RA,Crohn's disease, transplant rejection and graft-versus-host disease,according to the methods described herein, the present invention mayfurther comprise recommending a course of therapy based upon thediagnosis, prognosis, or prediction. In certain instances, the presentinvention may further comprise administering to a subject atherapeutically effective amount of an anti-TNFα drug useful fortreating one or more symptoms associated with the TNF-mediated diseaseor disorder. For therapeutic applications, the anti-TNF drug can beadministered alone or co-administered in combination with one or moreadditional anti-TNF drugs and/or one or more drugs that reduce theside-effects associated with the anti-TNF drug (e.g., animmunosuppressive agent). As such, the present invention advantageouslyenables a clinician to practice “personalized medicine” by guidingtreatment decisions and informing therapy selection and optimization foranti-TNFα drugs such that the right drug is given to the right patientat the right time.

The present invention is advantageous because it addresses and overcomescurrent limitations associated with the administration of anti-TNF drugssuch as infliximab, in part, by providing information useful for guidingtreatment decisions for those patients receiving or about to receiveanti-TNF drug therapy. In particular, the methods of the presentinvention find utility for selecting an appropriate anti-TNF therapy forinitial treatment, for determining when or how to adjust or modify(e.g., increase or decrease) the subsequent dose of an anti-TNF drug tooptimize therapeutic efficacy and/or to reduce toxicity, for determiningwhen or how to combine an anti-TNF drug (e.g., at an initial, increased,decreased, or same dose) with one or more immunosuppressive agents suchas methotrexate (MTX) or azathioprine (AZA), and/or for determining whenor how to change the current course of therapy (e.g., switch to adifferent anti-TNF drug or to a drug that targets a differentmechanism).

Accordingly, the present invention is particularly useful in thefollowing methods of improving patient management by guiding treatmentdecisions:

-   -   1. Crohn's disease prognostics: Treat patients most likely to        benefit from therapy    -   2. Anti-therapeutic antibody monitoring (ATM)+Biomarker-based        disease activity profiling    -   3. ATM sub-stratification    -   4. ATM with pharmacokinetic modeling    -   5. Monitor response and predict risk of relapse:        -   a. Avoid chronic maintenance therapy in patients with low            risk of recurrence        -   b. Markers of mucosal healing        -   c. Therapy selection: Whether to combine or not to combine            anti-TNF drug therapy with an immunosuppressive agent such            as MTX or AZA    -   6. Patient selection for biologics.

In some embodiments, the present invention provides a method formeasuring an inflammatory index for Crohn's Disease management for anindividual to optimize therapy, and predict response to the anti-TNFtherapeutic, the method comprising:

-   -   (a) chromatographically measuring anti-TNF therapeutics and        autoantibodies in a sample from the individual to determine        their concentration levels;    -   (b) chromatographically measuring anti-TNF therapeutics and        autoantibodies in a sample from the individual to determine        their concentration levels;    -   (c) comparing the measured values to an efficacy scale to        optimize therapy, and predict response to the anti-TNF        therapeutic.

In some embodiments, the present invention provides a method forpredicting the likelihood the concentration of an anti-TNF therapeuticduring the course of treatment will fall below a threshold value, themethod comprising:

-   -   (a) measuring a panel of markers selected from the group        consisting of 1) GM-CSF; 2) IL-2; 3) TNF-α; 4) sTNFRII; and 5)        the disease being situated in the small intestine; and    -   (b) predicting the likelihood the concentration of an anti-TNFα        therapeutic will fall below the threshold based upon the        concentration of the markers.

For the purpose of illustration only, Example 5 shows an exemplaryembodiment of the present invention In particular, a method ofpredicting the likelihood the concentration of an anti-TNF treatmentwill fall below a threshold value.

In some embodiments, the present invention provides a method forpredicting the likelihood the concentration of an anti-TNF therapeuticduring the course of treatment will fall below a threshold value, themethod comprising:

-   -   (a) measuring a panel of markers selected from the group        consisting of 1) GM-CSF; 2) IL-2; 3) TNF-α; 4) sTNFRII; and 5)        the disease being situated in the small intestine; and    -   (b) predicting the likelihood the concentration of an anti-TNF        therapeutic will fall below the threshold based upon the        concentration of the markers.

In other embodiments, the present invention provides a method forpredicting the likelihood that anti-drug antibodies will occur in anindividual on anti-TNF therapy, the method comprising:

-   -   (a) measuring a panel of markers selected from the group        consisting of t EGF, VEGF, IL-8, CRP and VCAM-1; and    -   (b) predicting the likelihood that anti-drug antibodies will        occur in an individual on anti-TNF therapy based on the        concentration of marker levels.

For the purpose of illustration only, Example 4 is an exemplaryembodiment of the present invention and demonstrates the detecting ofanti-drug antibodies to infliximab (ATI).

In other embodiments, the present invention provides a method formonitoring an infliximab treatment regimen, the method comprising:

-   -   (a) measuring infliximab and antidrug antibodies to infliximab        (ATI);    -   (b) measuring inflammatory markers CRP, SAA, ICAM, VCAM;    -   (c) measuring tissue repair marker VEGF; and    -   (d) correlating the measurements to therapeutic efficacy.

For the purpose of illustration only, Example 5 is an exemplaryembodiment of the present invention and shows a method of monitoring anIFX treatment regimen.

In other embodiments, the present invention provides a method fordetermining whether an individual is a candidate for combination therapywherein said individual is administered infliximab, the methodcomprising:

-   -   (a) measuring for the presence or absence of ATI in said        individual; and    -   (b) administering an immunosuppressant (e.g., MTX) is the        individual has significant levels of ATI.

In yet other embodiments, the method also includes measuring theconcentration level of CRP which is indicative of the presence of ATI.For the purpose of illustration only, Examples 6 and 7 show that thepresence and absence of ATI are predictive of responders andnon-responders of Remicade therapy. Examples 6 and 7 are exemplaryembodiments.

In yet other embodiments, the present invention provides a method formonitoring Crohn's disease activity, the method comprising:

-   -   (a) determining an inflammatory index comprising the measurement        of a panel of markers comprising VEGF in pg/ml, CRP in ng/ml,        SAA in ng/ml, ICAM in ng/ml and VCAM in ng/ml; and    -   (b) comparing the index to an efficacy scale to monitor and        mange disease.

For the purpose of illustration only, Example 9 is an exemplaryembodiment and shows use of the inflammatory index.

In particular embodiments, the present invention provides methods fordetermining the threshold of an anti-TNF drug such as IFX that can bestdiscriminate disease activity as measured by C-reactive protein (CRP)levels. For the purpose of illustration only, Example 12 shows that IFXdichotomized at a threshold of 3 μg/ml can be differentiated by CRP. Incertain instances, random IFX<3 and IFX≥3 μg/ml serum samples havehigher CRP in IFX<3 μg/ml at a 74% rate (ROC AUC). Example 12 also showsthat in ATI+ samples pairs, no significant difference in CRP between IFXgroups (above and below 3 μg/ml) was observed. In particular, CRP levelswere generally higher in ATI+ sample pairs, and CRP levels were higherin IFX<3 μg/ml for ATI− samples. Regression confirmed that CRP waspositively related to ATI and negatively related to IFX. As such, theinteraction corresponds to a CRP-IFX relationship that differs betweenATI+ and ATI−.

IV. Mucosal Healing Index

The methods of the present invention comprise monitoring therapyresponse and predicting risk of relapse. In some embodiments, themethods include detecting, measuring and/or determining the presenceand/or levels of markers of mucosal healing.

The gut mucosa plays a key role in barrier defense in addition tonutrient digestion, absorption and metabolism. The dynamic processes ofintestinal epithelial cell proliferation, migration, and apoptosis arehighly affected by general nutritional status, route of feeding, andadequacy of specific nutrients in the diet. However, with inflammatorydiseases of the gut, mucosal cell impairment can result in mucosalinjury or damage, thereby resulting in enhanced permeability tomacromolecules, increased bacterial translocation from the lumen, andstimulation of epithelial cell apoptosis.

Mucosal injury is a multi-faceted physiological process spanningmacroscopic to molecular levels. Mucosal injury includes the formationof macroscopically visible mucosal lesions detectable during endoscopy,granuloma formation and disruption of the muscularis layer at themicroscopic tissue level, epithelial apoptosis and infiltration ofactivated inflammatory and lymphocytic cells at the cellular level,increased epithelial permeability at a sub-cellular level, and gapjunction disruption at a molecular level.

Mucosal injury is likely initiated by a combination of endogenous andenvironmental factors. At first stage, it is believed that food-derivedcompounds, viral and bacterial-derived factors, as well as host-derivedfactors, may cause epithelial cell destruction and activation of innateand adaptive immunity. Damaged mucosa is initially infiltrated bydiverse inflammatory cells consisting of neutrophils, eosinophils, mastcells, inflammatory monocytes, activated macrophages and dendriticcells. Specific adaptive immune responses toward the intestinal floraare generated leading to the later recruitment of activated B cells,CD4+ and CD8+ T cells to the inflamed mucosa. Neutrophils secreteelastase which can result in extracellular matrix degradation of theepithelium. Likewise, T cells, macrophages and intestinal fibroblastsexpress inflammatory factors such as IL-1, IL-2, IL-6, IL-14, IL-17,TGFβ and TNFα that lead to extracellular matrix degradation, epithelialdamage, endothelial activation, and/or fibrosis stricture formation.Non-limiting examples of markers of mucosal injury include matrixmetalloproteases (MMPs) and markers of oxidative stress (e.g., iNOS,reactive oxygen metabolites).

A. Array of Mucosal Healing Markers

A variety of mucosal markers including growth factors are particularlyuseful in the methods of the present invention for personalizedtherapeutic management by selecting therapy, optimizing therapy,reducing toxicity, and/or monitoring the efficacy of therapeutictreatment with one or more therapeutic agents such as biologics (e.g.,anti-TNF drugs). In particular embodiments, the methods described hereinutilize the determination of a mucosal healing index based upon one ormore (a plurality of) mucosal healing markers such as growth factors(e.g., alone or in combination with biomarkers from other categories) toaid or assist in predicting disease course, selecting an appropriateanti-TNF drug therapy, optimizing anti-TNF drug therapy, reducingtoxicity associated with anti-TNF drug therapy, and/or monitoring theefficacy of therapeutic treatment with an anti-TNF drug.

As such, in certain embodiments, the determination of the presenceand/or level of one or more growth factors in a sample is useful in thepresent invention. As used herein, the term “growth factor” includes anyof a variety of peptides, polypeptides, or proteins that are capable ofstimulating cellular proliferation and/or cellular differentiation.

In some embodiments, mucosal healing markers include, but are notlimited to, growth factors, inflammatory markers, cellular adhesionmarkers, cytokines, anti-inflammatory markers, matrixmetalloproteinases, oxidative stress markers, and/or stress responsemarkers.

In some embodiments, mucosal healing markers include growth factors.Non-limiting examples of growth factors include amphiregulin (AREG),epiregulin (EREG), heparin binding epidermal growth factor (HB-EGF),hepatocye growth factor (HGF), heregulin-β1 (HRG) and isoforms,neuregulins (NRG1, NRG2, NRG3, NRG4), betacellulin (BTC), epidermalgrowth factor (EGF), insulin growth factor-1 (IGF-1), transforminggrowth factor (TGF), platelet-derived growth factor (PDGF), vascularendothelial growth factors (VEGF-A, VEGF-B, VEGF-C, VEGF-D), stem cellfactor (SCF), platelet derived growth factor (PDGF), soluble fms-liketyrosine kinase 1 (sFlt1), placenta growth factor (PIGF, PLGF or PGF),fibroblast growth factors (FGF1, FGF2, FGF7, FGF9), and combinationsthereof. In other embodiments, mucosal healing markers also includepigment epithelium-derived factor (PEDF, also known as SERPINF1),endothelin-1 (ET-1), keratinocyte growth factor (KGF; also known asFGF7), bone morphogenetic proteins (e.g., BMP1-BMP15), platelet-derivedgrowth factor (PDGF), nerve growth factor (NGF), β-nerve growth factor(β-NGF), neurotrophic factors (e.g., brain-derived neurotrophic factor(BDNF), neurotrophin 3 (NT3), neurotrophin 4 (NT4), etc.), growthdifferentiation factor-9 (GDF-9), granulocyte-colony stimulating factor(G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF),myostatin (GDF-8), erythropoietin (EPO), thrombopoietin (TPO), andcombinations thereof.

In other embodiments, mucosal healing markers also include cytokines.Non-limiting examples of cytokines that can be used to establish amucosal healing index include bFGF, TNF-α, IL-10, IL-12(p70), IL-1β,IL-2, IL-6, GM-CSF, IL-13, IFN-γ, TGF-β1, TGF-β2, TGF-β3, andcombinations thereof. Non-limiting examples of cellular adhesion markersinclude SAA, CRP, ICAM, VCAM, and combinations thereof. Non-limitingexamples of anti-inflammatory markers include IL-12p70, IL-10, andcombinations thereof.

In some embodiments, mucosal healing markers include markers specific tothe gastrointestinal tract including inflammatory markers and serologymarkers as described herein. Non-limiting examples include antibodies tobacterial antigens such as, e.g., OmpC, flagellins (cBir-1, Fla-A,Fla-X, etc.), I2, and others (pANCA, ASCA, etc.); anti-neutrophilantibodies, anti-Saccharomyces cerevisiae antibodies, and anti-microbiolantibodies.

The determination of markers of oxidative stress in a sample is alsouseful in the present invention. Non-limiting examples of markers ofoxidative stress include those that are protein-based or DNA-based,which can be detected by measuring protein oxidation and DNAfragmentation, respectively. Other examples of markers of oxidativestress include organic compounds such as malondialdehyde.

Oxidative stress represents an imbalance between the production andmanifestation of reactive oxygen species and a biological system'sability to readily detoxify the reactive intermediates or to repair theresulting damage. Disturbances in the normal redox state of tissues cancause toxic effects through the production of peroxides and freeradicals that damage all components of the cell, including proteins,lipids, and DNA. Some reactive oxidative species can even act asmessengers through a phenomenon called redox signaling.

In certain embodiments, derivatives of reactive oxidative metabolites(DROMs), ratios of oxidized to reduced glutathione (Eh GSH), and/orratios of oxidized to reduced cysteine (Eh CySH) can be used to quantifyoxidative stress. See, e.g., Neuman et al., Clin. Chem., 53:1652-1657(2007). Oxidative modifications of highly reactive cysteine residues inproteins such as tyrosine phosphatases and thioredoxin-related proteinscan also be detected or measured using a technique such as, e.g., massspectrometry (MS). See, e.g., Naito et al., Anti-Aging Medicine, 7(5):36-44 (2010). Other markers of oxidative stress includeprotein-bound acrolein as described, e.g., in Uchida et al., PNAS, 95(9) 4882-4887 (1998), the free oxygen radical test (FORT), whichreflects levels of organic hydroperoxides, and the redox potential ofthe reduced glutathione/glutathione disulfide couple, (Eh) GSH/GSSG.See, e.g., Abramson et al., Atherosclerosis, 178(1):115-21 (2005).

In some embodiments, matrix metalloproteinases (MMPs) include members ofa family of Zn²⁺-dependent extracellular matrix (ECM) degradingendopeptidases that are able to degrade all types of ECM proteins.Non-limiting examples of MMPs include MMP-1, MMP-2, MMP-3, MMP-7, MMP-8,MMP-9, MMP-12, MMP-13, MT1-MMP-1, and combinations thereof. It has beenshown that MMP-3 and MMP-9 are associated with mucosal injury andfistulae in CD patients (Baugh et al., Gastroenterology, 117: 814-822,(1999); Bailey et al., J. Clin. Pathol., 47: 113-116 (1994)). In someembodiments, stress response markers include markers of oxidativestress, such as reactive oxygen species (ROS), superoxide dismutase(SOD), catalase (CAT), and glutathione, and markers of endoplasmicreticulum (ER) stress. Non-limiting examples of markers of oxidativestress include those that are protein-based or DNA-based, which can bedetected by measuring protein oxidation and DNA fragmentation,respectively. In other embodiments, mucosal healing markers furtherinclude markers of oxidative DNA and/or protein damage. Non-limitingexamples of ER stress markers include markers of unfolded proteinresponse (e.g., ATF6, HSPA5, PDIA4, XBP1, IRE1, PERK, EIF2A, GADD34,GRP-78, phosphoylated JNK, caspase-12, caspase-3, and combinationsthereof).

The human amphiregulin (AREG) polypeptide sequence is set forth in,e.g., Genbank Accession Nos. NP_001648.1 and XP_001125684.1. The humanAREG mRNA (coding) sequence is set forth in, e.g., Genbank AccessionNos. NM_001657.2 and XM_001125684.3. One skilled in the art willappreciate that AREG is also known as AR, colorectum cell-derived growthfactor, CRDGF, SDGF, and AREGB.

The human epiregulin (EREG) polypeptide sequence is set forth in, e.g.,Genbank Accession No. NP_001423.1. The human EREG mRNA (coding) sequenceis set forth in, e.g., Genbank Accession No. NM_001432.2. One skilled inthe art will appreciate that EREG is also known as EPR.

The human heparin-binding EGF-like growth factor (HB-EGF) polypeptidesequence is set forth in, e.g., Genbank Accession No. NP_001936.1. Thehuman HB-EGF mRNA (coding) sequence is set forth in, e.g., GenbankAccession No. NM_001945.2. One skilled in the art will appreciate thatHB-EGF is also known as diphtheria toxin receptor, DT-R, HBEGF, DTR,DTS, and HEGFL.

The human hepatocyte growth factor (HGF) polypeptide sequence is setforth in, e.g., Genbank Accession Nos. NP_000592.3, NP_001010931.1,NP_001010932.1, NP_001010933.1, and NP_001010934.1. The human HGF mRNA(coding) sequence is set forth in, e.g., Genbank Accession Nos.NM_000601.4, NM_001010931.1, NM_001010932.1, NM_001010933.1 andNM_001010934.1. One skilled in the art will appreciate that HGF is alsoknown as scatter factor, SF, HPTA and hepatopoietin-A. One of skill willalso appreciate that HGF includes to all isoform variants.

The human neuregulin-1 (NRG1) polypeptide sequence is set forth in,e.g., Genbank Accession Nos., NP_001153467.1, NP_001153471.1,NP_001153473.1, NP_001153477.1, NP_039250.2, NP_039251.2, NP_039252.2,NP_039253.1, NP_039254.1, NP_039256.2, and NP_039258.1. The human NRG1mRNA (coding) sequence is set forth in, e.g., Genbank Accession No.NM_001159995.1, NM_001159999.1, NM_001160001.1, NM_001160005.1,NM_013956.3, NM_013957.3, NM_013958.3, NM_013959.3, NM_013960.3,NM_013962.2, and NM_013964.3. One skilled in the art will appreciatethat NRG1 is also known as GGF, HGL, HRGA, NDF, SMDF, ARIA,acetylcholine receptor-inducing activity, breast cancer celldifferentiation factor p45, glial growth factor, heregulin, HRG, neudifferentiation factor, and sensory and motor neuron-derived factor. Oneof skill will also appreciate that NRG1 includes to all isoformvariants.

The human neuregulin-2 (NRG2) polypeptide sequence is set forth in,e.g., Genbank Accession Nos. NP_001171864.1, NP_004874.1, NP_053584.1,NP_053585.1 and NP_053586.1. The human NRG2 mRNA (coding) sequence isset forth in, e.g., Genbank Accession Nos. NM_001184935.1, NM_004883.2,NM_013981.3, NM_013982.2 and NM_013983.2. One skilled in the art willappreciate that NRG2 is also known as NTAK, neural- and thymus-derivedactivator for ERBB kinases, DON-1, and divergent of neuregulin-1. One ofskill will also appreciate that NRG2 includes to all isoform variants.

The human neuregulin-3 (NRG3) polypeptide sequence is set forth in,e.g., Genbank Accession Nos. NP_001010848.2 and NP_001159445.1. Thehuman NRG3 mRNA (coding) sequence is set forth in, e.g., GenbankAccession Nos. NM_001010848.3 and NM_001165973.1. One skilled in the artwill appreciate that NRG2 includes to all isoform variants.

The human neuregulin-4 (NRG4) polypeptide sequence is set forth in,e.g., Genbank Accession No. NP_612640.1. The human NRG4 mRNA (coding)sequence is set forth in, e.g., Genbank Accession No. NM_138573.3. Oneskilled in the art will appreciate that NRG4 includes to all isoformvariants.

The human betacellulin (BTC) polypeptide sequence is set forth in, e.g.,Genbank Accession No. NP_001720.1. The human BTC mRNA (coding) sequenceis set forth in, e.g., Genbank Accession No. NM_001729.2. One skilled inthe art will appreciate that BTC includes to all isoform variants.

The human epidermal growth factor (EGF) polypeptide sequence is setforth in, e.g., Genbank Accession Nos. NP_001954.2 and NP_001171602.1.The human EGF mRNA (coding) sequence is set forth in, e.g., GenbankAccession Nos. NM_001963.4 and NM_001178131.1. One skilled in the artwill appreciate that EGF is also known as beta-urogastrone, urogastrone,URG, and HOMG4.

The human insulin-like growth factor (IGF) polypeptide sequence is setforth in, e.g., Genbank Accession Nos. NP_000609.1 and NP_001104755.1.The human IGF mRNA (coding) sequence is set forth in, e.g., GenbankAccession No. NM_000618.3 and NM_001111285.1. One skilled in the artwill appreciate that IGF includes to all isoform variants. One skilledin the art will also appreciate that IGF is also known as mechano growthfactor, MGF and somatomedin-C.

The human transforming growth factor alpha (TGF-α) polypeptide sequenceis set forth in, e.g., Genbank Accession Nos. NP_003227.1 andNP_001093161.1. The human TGF-α mRNA (coding) sequence is set forth in,e.g., Genbank Accession Nos. NM_003236.3 and NM_001099691.2. One skilledin the art will appreciate that TGF-α includes to all isoform variants.One skilled in the art will also appreciate that TGF-α is also known asEGF-like TGF, ETGF, and TGF type 1.

The human vascular endothelial growth factor (VEGF-A) polypeptidesequence is set forth in, e.g., Genbank Accession Nos. NP_001020537,NP_001020538, NP_001020539, NP_001020540, NP_001020541, NP_001028928,and NP_003367. The human VEGF-A mRNA (coding) sequence is set forth in,e.g., Genbank Accession No. NM_001025366, NM_001025367, NM_001025368,NM_001025369, NM_001025370, NM_001033756, and NM_003376. One skilled inthe art will appreciate that VEGF-A is also known as VPF, VEGFA, VEGF,and MGC70609. One skilled in the art will appreciate that VEGF-Aincludes to all isoform variants.

The human vascular endothelial growth factor (VEGF-B) polypeptidesequence is set forth in, e.g., Genbank Accession Nos. NP_001230662, andNP_003368. The human VEGF-B mRNA (coding) sequence is set forth in,e.g., Genbank Accession Nos. NM_001243733 and NM_003377. One skilled inthe art will appreciate that VEGF-B is also known as VEGFB, VEGF-relatedfactor, and VRF. One skilled in the art will appreciate that VEGF-Bincludes to all isoform variants.

The human vascular endothelial growth factor (VEGF-C) polypeptidesequence is set forth in, e.g., Genbank Accession No. NP_005420. Thehuman VEGF-C mRNA (coding) sequence is set forth in, e.g., GenbankAccession No. NM_005429. One skilled in the art will appreciate thatVEGF-C is also known as Flt4 ligand, Flt-4-L, VRP and vascularendothelial growth factor-realted protein. One skilled in the art willappreciate that VEGF-C includes to all isoform variants.

The human fibroblast growth factor 1 (FGF1) polypeptide sequence is setforth in, e.g., Genbank Accession Nos. NP_000791, NP_001138364,NP_001138406, NP_001138407, NP_001138407, NP_149127, and NP_149128. Thehuman FGF1 mRNA (coding) sequence is set forth in, e.g., GenbankAccession Nos. NM_000800, NM_001144892, NM_001144934, NM_001144934,NM_001144935, NM_033136 and NM_033137. One skilled in the art willappreciate that FGF1 is also known as FGFA, FGF-1, acidic fibroblastgrowth factor, aFGF, endothelial cell growth factor, ECGF,heparin-binding growth factor 1, and HB-EGF1. One skilled in the artwill appreciate that FGF1 includes to all isoform variants.

The human basic fibroblast growth factor (bFGF) polypeptide sequence isset forth in, e.g., Genbank Accession No. NP_001997.5. The human bFGFmRNA (coding) sequence is set forth in, e.g., Genbank Accession No.NM_002006.4. One skilled in the art will appreciate that bFGF is alsoknown as FGF2, FGFB, and HBGF-2.

The human fibroblast growth factor 7 (FGF7) polypeptide sequence is setforth in, e.g., Genbank Accession No. NP_002000.1. The human FGF7 mRNA(coding) sequence is set forth in, e.g., Genbank Accession No.NM_002009.3. One skilled in the art will appreciate that FGF7 is alsoknown as FGF-7, HBGF-7 and keratinocyte growth factor.

The human fibroblast growth factor 9 (FGF9) polypeptide sequence is setforth in, e.g., Genbank Accession No. NP_002001.1. The human FGF9 mRNA(coding) sequence is set forth in, e.g., Genbank Accession No.NM_002010.2. One skilled in the art will appreciate that FGF9 is alsoknown as FGF-9, GAF, and HBGF-9.

The human TNF-related weak inducer of apoptosis (TWEAK) polypeptidesequence is set forth in, e.g., Genbank Accession No. NP_003800.1. Thehuman TWEAK mRNA (coding) sequence is set forth in, e.g., GenbankAccession No. NM_003809.2. One skilled in the art will appreciate thatTWEAK is also known as TNF12, APO3 ligand, APO3L, DR3LG, andUNQ181/PRO207.

In certain instances, the presence or level of a particular mucosalhealing marker such as a growth factor is detected at the level of mRNAexpression with an assay such as, for example, a hybridization assay oran amplification-based assay. In certain other instances, the presenceor level of a particular growth factor is detected at the level ofprotein expression using, for example, an immunoassay (e.g., ELISA) oran immunohistochemical assay. In an exemplary embodiment, the presenceor level of a particular growth factor is detected using a multiplexedimmunoarray, such as a Collaborative Enzyme Enhanced ReactiveImmunoAssay (CEER), also known as the Collaborative ProximityImmunoassay (COPIA). CEER is described in the following patent documentswhich are herein incorporated by reference in their entirety for allpurposes: PCT Publication No. WO 2008/036802; PCT Publication No. WO2009/012140; PCT Publication No. WO 2009/108637; PCT Publication No. WO2010/132723; PCT Publication No. WO 2011/008990; and PCT Application No.PCT/US2010/053386, filed Oct. 20, 2010. Suitable ELISA kits fordetermining the presence or level of a growth factor in a serum, plasma,saliva, or urine sample are available from, e.g., Antigenix America Inc.(Huntington Station, N.Y.), Promega (Madison, Wis.), R&D Systems, Inc.(Minneapolis, Minn.), Invitrogen (Camarillo, Calif.), CHEMICONInternational, Inc. (Temecula, Calif.), Neogen Corp. (Lexington, Ky.),PeproTech (Rocky Hill, N.J.), Alpco Diagnostics (Salem, N.H.), PierceBiotechnology, Inc. (Rockford, Ill.), and/or Abazyme (Needham, Mass.).

In particular embodiments, at least one or a plurality (e.g., 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or 21, suchas, e.g., a panel or an array) of the following growth factor markerscan be detected (e.g., alone or in combination with biomarkers fromother categories) to aid or assist in predicting disease course, and/orto improve the accuracy of selecting therapy, optimizing therapy,reducing toxicity, and/or monitoring the efficacy of therapeutictreatment to anti-TNF drug therapy: AREG, EREG, HB-EGF, HGF, NRG1, NRG2,NRG3, NRG4, BTC, EGF, IGF, TGF-α, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1,FGF2, FGF7, FGF9, TWEAK and combinations thereof.

B. Mucosal Healing Index

In certain aspects, the present invention provides an algorithmic-basedanalysis of one or a plurality of (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or more) mucosal healing markersto improve the accuracy of selecting therapy, optimizing therapy,reducing toxicity, and/or monitoring the efficacy of therapeutictreatment to anti-TNFα drug therapy.

A single statistical algorithm or a combination of two or morestatistical algorithms described herein can then be applied to thepresence or concentration level of the mucosal healing markers detected,measured, or determined in the sample to thereby select therapy,optimize therapy, reduce toxicity, or monitor the efficacy oftherapeutic treatment with an anti-TNFα drug. As such, the methods ofthe invention find utility in determining patient management bydetermining patient immune status.

In some embodiments, the statistical algorithm comprises a learningstatistical classifier system. In some instances, the learningstatistical classifier system is selected from the group consisting of arandom forest, classification and regression tree, boosted tree, neuralnetwork, support vector machine, general chi-squared automaticinteraction detector model, interactive tree, multiadaptive regressionspline, machine learning classifier, and combinations thereof. Incertain instances, the statistical algorithm comprises a single learningstatistical classifier system. In other embodiments, the statisticalalgorithm comprises a combination of at least two learning statisticalclassifier systems. In some instances, the at least two learningstatistical classifier systems are applied in tandem. Non-limitingexamples of statistical algorithms and analysis suitable for use in theinvention are described in International Application No.PCT/US2011/056777, filed Oct. 18, 2011, the disclosure of which ishereby incorporated by reference in its entirety for all purposes.

Preferably, mucosal healing index is an empirically derivedexperimentally prepared index of values. In some instances, the index ofvalues is transformed from an array of control measurements that wereexperimentally determined. In one aspect, the concentration of markersor their measured concentration values are transformed into an index byan algorithm resident on a computer. In certain aspects, the index is asynthetic or human derived output, score, or cut off value(s), whichexpresses the biological data in numerical terms. The index can be usedto determine or make or aid in making a clinical decision. A mucosalhealing index can be measured multiple times over the course of time. Inone aspect, the algorithm can be trained with known samples andthereafter validated with samples of known identity.

In further embodiments, the method for assessing or measuring mucosalhealing further comprises comparing the determined level of the mucosalhealing marker present in a sample to an index value or cutoff value orreference value or threshold value, wherein the level of the mucosalhealing marker above or below that value is predictive or indicative ofan increased or higher likelihood of the subject either undergoingmucosal healing or not undergoing mucosal healing. One skilled in theart will understand that the index value or cutoff value or referencevalue or threshold value is in units such as mg/ml, μg/ml, ng/ml, pg/ml,fg/ml, EU/ml, or U/ml depending on the marker of interest that is beingmeasured.

In some embodiments, the mucosal healing index control is a mucosalhealing index derived from a healthy individual, or an individual whohas progressed from a disease state to a healthy state. Alternatively,the control can be an index representing a time course of a morediseased state or healthy to disease.

In some embodiments, the methods of determining the course of therapyand the like include the use of an empirically derived index, score oranalysis to select for example, selecting a dose of drug, selecting anappropriate drug, or a course or length of therapy, a therapy regimen,or maintenance of an existing drug or dose. In certain aspects, aderived or measured index can be used to determine the course oftherapy.

Understanding the clinical course of disease will enable physicians tomake better informed treatment decisions for their inflammatory diseasepatients (e.g., IBD, Crohn's disease or ulcerative colitis) and may helpto direct new drug development in the future. The ideal mucosal healingmarker(s) for use in the mucosal healing index described herein shouldbe able to identify individuals at risk for the disease and should bedisease-specific. Moreover, mucosal healing marker(s) should be able todetect disease activity and monitor the effect of treatment; and shouldhave a predictive value towards relapse or recurrence of the disease.Predicting disease course, however, has now been expanded beyond justdisease recurrence, but perhaps more importantly to include predictorsof disease complications including surgery. The present invention isparticularly advantageous because it provides indicators of mucosalhealing and enables a prediction of the risk of relapse in thosepatients in remission. In addition, the mucosal healing markers andmucosal healing index of present invention have enormous implicationsfor patient management as well as therapeutic decision-making and wouldaid or assist in directing the appropriate therapy to those patients whowould most likely benefit from it and avoid the expense and potentialtoxicity of chronic maintenance therapy in those who have a low risk ofrecurrence.

I. DISEASE ACTIVITY PROFILE

As described herein, the disease activity profile (DAP) of the presentinvention can advantageously be used in methods for personalizedtherapeutic management of a disease in order to optimize therapy and/ormonitor therapeutic efficacy. In certain embodiments, the methods of theinvention can improve the accuracy of selecting therapy, optimizingtherapy, reducing toxicity, and/or monitoring the efficacy oftherapeutic treatment to anti-TNF drug therapy. In particularembodiments, the DAP is determined by measuring an array of one or aplurality of (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more) markersat a plurality of time points over the course of therapy with atherapeutic antibody (e.g., anti-TNF drug) to determine a DAP, whereinthe DAP comprises a representation of the concentration level of eachmarker over time. In certain embodiments, the DAP may comprise arepresentation of the presence or absence, concentration (e.g.,expression) level, activation (e.g., phosphorylation) level, and/orvelocity value (e.g., change in slope of the level of a particularmarker) of each marker over time. As such, the methods of the presentinvention find utility in determining patient management by determiningpatient immune status.

In certain instances, a single statistical algorithm or a combination oftwo or more statistical algorithms can be applied to the concentrationlevel of each marker over the course of therapy or to the DAP itself.

Understanding the clinical course of disease enables physicians to makebetter informed treatment decisions for their inflammatory diseasepatients (e.g., IBD (e.g., Crohn's disease), rheumatoid arthritis (RA),others) and helps to direct new drug development. The ideal biomarker(s)for use in the disease activity profile described herein is able toidentify individuals at risk for the disease and is disease-specific.Moreover, the biomarker(s) are able to detect disease activity andmonitor the effect of treatment; and have a predictive value towardsrelapse or recurrence of the disease. Predicting disease course,however, has now been expanded beyond just disease recurrence, but moreimportantly to include predictors of disease complications includingsurgery. The present invention is particularly advantageous because itprovides indicators of disease activity and/or severity and enables aprediction of the risk of relapse in those patients in remission. Inaddition, the biomarkers and disease activity profile of the presentinvention have enormous implications for patient management, as well astherapeutic decision-making, and aid or assist in directing theappropriate therapy to patients who most likely will benefit from it andavoid the expense and potential toxicity of chronic maintenance therapyin those who have a low risk of recurrence.

As a non-limiting example, the disease activity profile (DAP) in oneembodiment comprises detecting, measuring, or determining the presence,level (concentration (e.g., total) and/or activation (e.g.,phosphorylation)), or genotype of one or more specific biomarkers in oneor more of the following categories of biomarkers:

-   -   (1) Drug levels (e.g., anti-TNF drug levels);    -   (2) Anti-drug antibody (ADA) levels (e.g., level of autoantibody        to an anti-TNF drug);    -   (3) Inflammatory markers;    -   (4) Anti-inflammatory markers; and/or    -   (5) Tissue repair markers.

Non-limiting examples of additional and/or alternative markers in whichthe presence, level (concentration (e.g., total) and/or activation(e.g., phosphorylation)), or genotype can be measured include:

-   -   (6) Serology (e.g., immune markers);    -   (7) Markers of oxidative stress;    -   (8) Cell surface receptors (e.g., CD64, others);    -   (9) Signaling pathways;    -   (10) kel, or the elimination rate constant of a drug such as a        therapeutic antibody (e.g., infliximab); and/or    -   (11) Other markers (e.g., genetic markers such as inflammatory        pathway genes).

A. Anti-TNF Drug Levels & Anti-Drug Antibody (ADA) Levels

In some embodiments, the disease activity profile (DAP) comprisesdetermining the presence and/or level of anti-TNF drug (e.g., level offree anti-TNFα therapeutic antibody such as infliximab) and/or anti-drugantibody (ADA) (e.g., level of autoantibody to the anti-TNF drug such asHACA) in a patient sample (e.g., a serum sample from a patient onanti-TNF drug therapy) at multiple time points, e.g., before, during,and/or after the course of therapy.

In particular embodiments, the presence and/or level of anti-TNF drugand/or ADA is determined with a homogeneous mobility shift assay usingsize exclusion chromatography. This method, which is described in PCTApplication No. PCT/US2010/054125, filed Oct. 26, 2010, the disclosureof which is hereby incorporated by reference in its entirety for allpurposes, is particularly advantageous for measuring the presence orlevel of TNFα inhibitors as well as autoantibodies (e.g., HACA, HAHA,etc.) that are generated against them.

In one embodiment, the method for detecting the presence of an anti-TNFαantibody in a sample comprises:

-   -   (a) contacting labeled TNFα with a sample having or suspected of        having an anti-TNFα antibody to form a labeled complex with the        anti-TNFα antibody;    -   (b) subjecting the labeled complex to size exclusion        chromatography to separate the labeled complex; and    -   (c) detecting the labeled complex, thereby detecting the        anti-TNF antibody.

In certain instances, the methods are especially useful for thefollowing anti-TNFα antibodies: REMICADE™ (infliximab), ENBREL™(etanercept), HUMIRA™ (adalimumab), and CIMZIA® (certolizumab pegol).

Tumor necrosis factor α (TNFα) is a cytokine involved in systemicinflammation and is a member of a group of cytokines that stimulate theacute phase reaction. The primary role of TNFα is in the regulation ofimmune cells. TNFα is also able to induce apoptotic cell death, toinduce inflammation, and to inhibit tumorigenesis and viral replication.TNF is primarily produced as a 212-amino acid-long type II transmembraneprotein arranged in stable homotrimers.

The terms “TNF”, “TNFα,” and “TNF-α,” as used herein, are intended toinclude a human cytokine that exists as a 17 kDa secreted form and a 26kDa membrane associated form, the biologically active form of which iscomposed of a trimer of noncovalently bound 17 kDa molecules. Thestructure of TNF-α is described further in, for example, Jones, et al.(1989) Nature, 338:225-228. The term TNF-α is intended to include human,a recombinant human TNF-α (rhTNF-α), or at least about 80% identity tothe human TNFα protein. Human TNFα consists of a 35 amino acid (aa)cytoplasmic domain, a 21 aa transmembrane segment, and a 177 aaextracellular domain (ECD) (Pennica, D. et al. (1984) Nature 312:724).Within the ECD, human TNFα shares 97% aa sequence identity with rhesusand 71% 92% with bovine, canine, cotton rat, equine, feline, mouse,porcine, and rat TNFα. TNFα can be prepared by standard recombinantexpression methods or purchased commercially (R & D Systems, Catalog No.210-TA, Minneapolis, Minn.).

In certain instances, after the TNF α antibody is detected, the TNF αantibody is measured using a standard curve.

In another embodiment, the method for detecting an autoantibody to ananti-TNFα antibody in a sample comprises:

-   -   (a) contacting labeled anti-TNFα antibody with the sample to        form a labeled complex with the autoantibody;    -   (b) subjecting the labeled complex to size exclusion        chromatography to separate the labeled complex; and    -   (c) detecting the labeled complex, thereby detecting the        autoantibody.

In certain instances, the autoantibodies include human anti-chimericantibodies (HACA), human anti-humanized antibodies (HAHA), and humananti-mouse antibodies (HAMA).

Non-limiting examples of other methods for determining the presenceand/or level of anti-TNF drug and/or anti-drug antibodies (ADA) includeenzyme-linked immunosorbent assays (ELISAs) such as bridging ELISAs. Forexample, the Infliximab ELISA from Matriks Biotek Laboratories detectsfree infliximab in serum and plasma samples, and the HACA ELISA fromPeaceHealth Laboratories detects HACA in serum samples.

B. Inflammatory Markers

Although disease course of an inflammatory disease is typically measuredin terms of inflammatory activity by noninvasive tests using white bloodcell count, this method has a low specificity and shows limitedcorrelation with disease activity.

As such, in certain embodiments, a variety of inflammatory markers,including biochemical markers, serological markers, protein markers,genetic markers, and/or other clinical or echographic characteristics,are particularly useful in the methods of the present invention forpersonalized therapeutic management by selecting therapy, optimizingtherapy, reducing toxicity, and/or monitoring the efficacy oftherapeutic treatment with one or more therapeutic agents such asbiologics (e.g., anti-TNF drugs). In particular embodiments, the methodsdescribed herein utilize the determination of a disease activity profile(DAP) based upon one or more (a plurality of) inflammatory markers(e.g., alone or in combination with biomarkers from other categories) toaid or assist in predicting disease course, selecting an appropriateanti-TNF drug therapy, optimizing anti-TNF drug therapy, reducingtoxicity associated with anti-TNF drug therapy, and/or monitoring theefficacy of therapeutic treatment with an anti-TNF drug.

Non-limiting examples of inflammatory markers include cytokines,chemokines, acute phase proteins, cellular adhesion molecules, S100proteins, and/or other inflammatory markers. In preferred embodiments,the inflammatory markers comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20, 25, or more cytokines. In one particular embodiment, thecytokines are at least 1, 2, 3, 4, 5, 6, 7, or all 8 of the following:GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, and sTNF RII.

1. Cytokines and Chemokines

The determination of the presence or level of at least one cytokine orchemokine in a sample is particularly useful in the present invention.As used herein, the term “cytokine” includes any of a variety ofpolypeptides or proteins secreted by immune cells that regulate a rangeof immune system functions and encompasses small cytokines such aschemokines. The term “cytokine” also includes adipocytokines, whichcomprise a group of cytokines secreted by adipocytes that function, forexample, in the regulation of body weight, hematopoiesis, angiogenesis,wound healing, insulin resistance, the immune response, and theinflammatory response.

In certain embodiments, the presence or level of at least one cytokineincluding, but not limited to, granulocyte-macrophage colony-stimulatingfactor (GM-CSF), IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, soluble tumornecrosis factor-α receptor II (sTNF RII), TNF-related weak inducer ofapoptosis (TWEAK), osteoprotegerin (OPG), IFN-α, IFN-β, IL-1α, IL-1receptor antagonist (IL-1ra), IL-4, IL-5, soluble IL-6 receptor(sIL-6R), IL-7, IL-9, IL-12, IL-13, IL-15, IL-17, IL-23, and IL-27 isdetermined in a sample.

In certain other embodiments, the presence or level of at least onechemokine such as, for example, CXCL1/GRO1/GROα, CXCL2/GRO2, CXCL3/GRO3,CXCL4/PF-4, CXCL5/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG,CXCL10/IP-10, CXCL11/1-TAC, CXCL12/SDF-1, CXCL13/BCA-1, CXCL14/BRAK,CXCL15, CXCL16, CXCL17/DMC, CCL1, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β,CCL5/RANTES, CCL6/C10, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL10,CCL11/Eotaxin, CCL12/MCP-5, CCL13/MCP-4, CCL14/HCC-1, CCL15/MIP-5,CCL16/LEC, CCL17/TARC, CCL18/MIP-4, CCL19/MIP-3β, CCL20/MIP-3α,CCL21/SLC, CCL22/MDC, CCL23/MPIF1, CCL24/Eotaxin-2, CCL25/TECK,CCL26/Eotaxin-3, CCL27/CTACK, CCL28/MEC, CL1, CL2, and CX₃CL1 isdetermined in a sample. In certain further embodiments, the presence orlevel of at least one adipocytokine including, but not limited to,leptin, adiponectin, resistin, active or total plasminogen activatorinhibitor-1 (PAI-1), visfatin, and retinol binding protein 4 (RBP4) isdetermined in a sample. Preferably, the presence or level of GM-CSF,IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, sTNF RII, and/or other cytokinesor chemokines is determined.

In certain instances, the presence or level of a particular cytokine orchemokine is detected at the level of mRNA expression with an assay suchas, for example, a hybridization assay or an amplification-based assay.In certain other instances, the presence or level of a particularcytokine or chemokine is detected at the level of protein expressionusing, for example, an immunoassay (e.g., ELISA) or animmunohistochemical assay. Suitable ELISA kits for determining thepresence or level of a cytokine or chemokine of interest in a serum,plasma, saliva, or urine sample are available from, e.g., R&D Systems,Inc. (Minneapolis, Minn.), Neogen Corp. (Lexington, Ky.), AlpcoDiagnostics (Salem, N.H.), Assay Designs, Inc. (Ann Arbor, Mich.), BDBiosciences Pharmingen (San Diego, Calif.), Invitrogen (Camarillo,Calif.), Calbiochem (San Diego, Calif.), CHEMICON International, Inc.(Temecula, Calif.), Antigenix America Inc. (Huntington Station, N.Y.),QIAGEN Inc. (Valencia, Calif.), Bio-Rad Laboratories, Inc. (Hercules,Calif.), and/or Bender MedSystems Inc. (Burlingame, Calif.).

The human IL-6 polypeptide sequence is set forth in, e.g., GenbankAccession No. NP_000591. The human IL-6 mRNA (coding) sequence is setforth in, e.g., Genbank Accession No. NM_000600. One skilled in the artwill appreciate that IL-6 is also known as interferon beta 2 (IFNB2),HGF, HSF, and BSF2.

The human IL-1β polypeptide sequence is set forth in, e.g., GenbankAccession No. NP_000567. The human IL-1β mRNA (coding) sequence is setforth in, e.g., Genbank Accession No. NM_000576. One skilled in the artwill appreciate that IL-1β is also known as IL1F2 and IL-1beta.

The human IL-8 polypeptide sequence is set forth in, e.g., GenbankAccession NP_000575. The human IL-8 mRNA (coding) sequence is set forthin, e.g., Genbank Accession No. NM_000584. One skilled in the art willappreciate that IL-8 is also known as CXCL8, K60, NAP, GCP1, LECT, LUCT,NAP1, 3-10C, GCP-1, LYNAP, MDNCF, MONAP, NAP-1, SCYB8, TSG-1, AMCF-1,and b-ENAP.

The human TWEAK polypeptide sequence is set forth in, e.g., GenbankAccession Nos. NP_003800 and AAC51923. The human TWEAK mRNA (coding)sequence is set forth in, e.g., Genbank Accession Nos. NM_003809 andBC104420. One skilled in the art will appreciate that TWEAK is alsoknown as tumor necrosis factor ligand superfamily member 12 (TNFSF12),APO3 ligand (APO3L), CD255, DR3 ligand, growth factor-inducible 14(Fn14) ligand, and UNQ181/PRO207.

2. Acute Phase Proteins

The determination of the presence or level of one or more acute-phaseproteins in a sample is also useful in the present invention.Acute-phase proteins are a class of proteins whose plasma concentrationsincrease (positive acute-phase proteins) or decrease (negativeacute-phase proteins) in response to inflammation. This response iscalled the acute-phase reaction (also called acute-phase response).Examples of positive acute-phase proteins include, but are not limitedto, C-reactive protein (CRP), D-dimer protein, mannose-binding protein,alpha 1-antitrypsin, alpha 1-antichymotrypsin, alpha 2-macroglobulin,fibrinogen, prothrombin, factor VIII, von Willebrand factor,plasminogen, complement factors, ferritin, serum amyloid P component,serum amyloid A (SAA), orosomucoid (alpha 1-acid glycoprotein, AGP),ceruloplasmin, haptoglobin, and combinations thereof. Non-limitingexamples of negative acute-phase proteins include albumin, transferrin,transthyretin, transcortin, retinol-binding protein, and combinationsthereof. Preferably, the presence or level of CRP and/or SAA isdetermined.

In certain instances, the presence or level of a particular acute-phaseprotein is detected at the level of mRNA expression with an assay suchas, for example, a hybridization assay or an amplification-based assay.In certain other instances, the presence or level of a particularacute-phase protein is detected at the level of protein expressionusing, for example, an immunoassay (e.g., ELISA) or animmunohistochemical assay. For example, a sandwich colorimetric ELISAassay available from Alpco Diagnostics (Salem, N.H.) can be used todetermine the level of CRP in a serum, plasma, urine, or stool sample.Similarly, an ELISA kit available from Biomeda Corporation (Foster City,Calif.) can be used to detect CRP levels in a sample. Other methods fordetermining CRP levels in a sample are described in, e.g., U.S. Pat.Nos. 6,838,250 and 6,406,862; and U.S. Patent Publication Nos.20060024682 and 20060019410. Additional methods for determining CRPlevels include, e.g., immunoturbidimetry assays, rapid immunodiffusionassays, and visual agglutination assays. Suitable ELISA kits fordetermining the presence or level of SAA in a sample such as serum,plasma, saliva, urine, or stool are available from, e.g., AntigenixAmerica Inc. (Huntington Station, N.Y.), Abazyme (Needham, Mass.), USCNLife (Missouri City, Tex.), and/or U.S. Biological (Swampscott, Mass.).

C-reactive protein (CRP) is a protein found in the blood in response toinflammation (an acute-phase protein). CRP is typically produced by theliver and by fat cells (adipocytes). It is a member of the pentraxinfamily of proteins. The human CRP polypeptide sequence is set forth in,e.g., Genbank Accession No. NP_000558. The human CRP mRNA (coding)sequence is set forth in, e.g., Genbank Accession No. NM_000567. Oneskilled in the art will appreciate that CRP is also known as PTX1,MGC88244, and MGC149895.

Serum amyloid A (SAA) proteins are a family of apolipoproteinsassociated with high-density lipoprotein (HDL) in plasma. Differentisoforms of SAA are expressed constitutively (constitutive SAAs) atdifferent levels or in response to inflammatory stimuli (acute phaseSAAs). These proteins are predominantly produced by the liver. Theconservation of these proteins throughout invertebrates and vertebratessuggests SAAs play a highly essential role in all animals. Acute phaseserum amyloid A proteins (A-SAAs) are secreted during the acute phase ofinflammation. The human SAA polypeptide sequence is set forth in, e.g.,Genbank Accession No. NP_000322. The human SAA mRNA (coding) sequence isset forth in, e.g., Genbank Accession No. NM_000331. One skilled in theart will appreciate that SAA is also known as PIG4, TP5314, MGC1111216,and SAA1.

3. Cellular Adhesion Molecules (IgSF CAMs)

The determination of the presence or level of one or more immunoglobulinsuperfamily cellular adhesion molecules in a sample is also useful inthe present invention. As used herein, the term “immunoglobulinsuperfamily cellular adhesion molecule” (IgSF CAM) includes any of avariety of polypeptides or proteins located on the surface of a cellthat have one or more immunoglobulin-like fold domains, and whichfunction in intercellular adhesion and/or signal transduction. In manycases, IgSF CAMs are transmembrane proteins. Non-limiting examples ofIgSF CAMs include Neural Cell Adhesion Molecules (NCAMs; e.g., NCAM-120,NCAM-125, NCAM-140, NCAM-145, NCAM-180, NCAM-185, etc.), IntercellularAdhesion Molecules (ICAMs, e.g., ICAM-1, ICAM-2, ICAM-3, ICAM-4, andICAM-5), Vascular Cell Adhesion Molecule-1 (VCAM-1),Platelet-Endothelial Cell Adhesion Molecule-1 (PECAM-1), L1 CellAdhesion Molecule (L1CAM), cell adhesion molecule with homology to L1CAM(close homolog of L1) (CHL1), sialic acid binding Ig-like lectins(SIGLECs; e.g., SIGLEC-1, SIGLEC-2, SIGLEC-3, SIGLEC-4, etc.), Nectins(e.g., Nectin-1, Nectin-2, Nectin-3, etc.), and Nectin-like molecules(e.g., Necl-1, Necl-2, Necl-3, Necl-4, and Necl-5). Preferably, thepresence or level of ICAM-1 and/or VCAM-1 is determined.

ICAM-1 is a transmembrane cellular adhesion protein that is continuouslypresent in low concentrations in the membranes of leukocytes andendothelial cells. Upon cytokine stimulation, the concentrations greatlyincrease. ICAM-1 can be induced by IL-1 and TNFα and is expressed by thevascular endothelium, macrophages, and lymphocytes. In IBD,proinflammatory cytokines cause inflammation by upregulating expressionof adhesion molecules such as ICAM-1 and VCAM-1. The increasedexpression of adhesion molecules recruit more lymphocytes to theinfected tissue, resulting in tissue inflammation (see, Goke et al., J.,Gastroenterol., 32:480 (1997); and Rijcken et al., Gut, 51:529 (2002)).ICAM-1 is encoded by the intercellular adhesion molecule 1 gene (ICAM1;Entrez GeneID:3383; Genbank Accession No. NM_000201) and is producedafter processing of the intercellular adhesion molecule 1 precursorpolypeptide (Genbank Accession No. NP_000192).

VCAM-1 is a transmembrane cellular adhesion protein that mediates theadhesion of lymphocytes, monocytes, eosinophils, and basophils tovascular endothelium. Upregulation of VCAM-1 in endothelial cells bycytokines occurs as a result of increased gene transcription (e.g., inresponse to Tumor necrosis factor-alpha (TNFα) and Interleukin-1(IL-1)). VCAM-1 is encoded by the vascular cell adhesion molecule 1 gene(VCAM1; Entrez GeneID:7412) and is produced after differential splicingof the transcript (Genbank Accession No. NM_001078 (variant 1) orNM_080682 (variant 2)), and processing of the precursor polypeptidesplice isoform (Genbank Accession No. NP_001069 (isoform a) or NP_542413(isoform b)).

In certain instances, the presence or level of an IgSF CAM is detectedat the level of mRNA expression with an assay such as, for example, ahybridization assay or an amplification-based assay. In certain otherinstances, the presence or level of an IgSF CAM is detected at the levelof protein expression using, for example, an immunoassay (e.g., ELISA)or an immunohistochemical assay. Suitable antibodies and/or ELISA kitsfor determining the presence or level of ICAM-1 and/or VCAM-1 in asample such as a tissue sample, biopsy, serum, plasma, saliva, urine, orstool are available from, e.g., Invitrogen (Camarillo, Calif.), SantaCruz Biotechnology, Inc. (Santa Cruz, Calif.), and/or Abcam Inc.(Cambridge, Mass.).

4. S100 Proteins

The determination of the presence or level of at least one S100 proteinin a sample is also useful in the present invention. As used herein, theterm “S100 protein” includes any member of a family of low molecularmass acidic proteins characterized by cell-type-specific expression andthe presence of 2 EF-hand calcium-binding domains. There are at least 21different types of S100 proteins in humans. The name is derived from thefact that S100 proteins are 100% soluble in ammonium sulfate at neutralpH. Most S100 proteins are homodimeric, consisting of two identicalpolypeptides held together by non-covalent bonds. Although S100 proteinsare structurally similar to calmodulin, they differ in that they arecell-specific, expressed in particular cells at different levelsdepending on environmental factors. S-100 proteins are normally presentin cells derived from the neural crest (e.g., Schwann cells,melanocytes, glial cells), chondrocytes, adipocytes, myoepithelialcells, macrophages, Langerhans cells, dendritic cells, andkeratinocytes. S100 proteins have been implicated in a variety ofintracellular and extracellular functions such as the regulation ofprotein phosphorylation, transcription factors, Ca²⁺ homeostasis, thedynamics of cytoskeleton constituents, enzyme activities, cell growthand differentiation, and the inflammatory response.

Calgranulin is an S100 protein that is expressed in multiple cell types,including renal epithelial cells and neutrophils, and are abundant ininfiltrating monocytes and granulocytes under conditions of chronicinflammation. Examples of calgranulins include, without limitation,calgranulin A (also known as S100A8 or MRP-8), calgranulin B (also knownas S100A9 or MRP-14), and calgranulin C (also known as S100A12).

In certain instances, the presence or level of a particular S100 proteinis detected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular S100protein is detected at the level of protein expression using, forexample, an immunoassay (e.g., ELISA) or an immunohistochemical assay.Suitable ELISA kits for determining the presence or level of an S100protein such as calgranulin A (S100A8), calgranulin B (S100A9), orcalgranulin C(S100A12) in a serum, plasma, or urine sample are availablefrom, e.g., Peninsula Laboratories Inc. (San Carlos, Calif.) and Hycultbiotechnology b.v. (Uden, The Netherlands).

Calprotectin, the complex of S100A8 and S100A9, is a calcium- andzinc-binding protein in the cytosol of neutrophils, monocytes, andkeratinocytes. Calprotectin is a major protein in neutrophilicgranulocytes and macrophages and accounts for as much as 60% of thetotal protein in the cytosol fraction in these cells. It is therefore asurrogate marker of neutrophil turnover. Its concentration in stoolcorrelates with the intensity of neutrophil infiltration of theintestinal mucosa and with the severity of inflammation. In someinstances, calprotectin can be measured with an ELISA using small(50-100 mg) fecal samples (see, e.g., Johne et al., Scand JGastroenterol., 36:291-296 (2001)).

5. Other Inflammatory Markers

The determination of the presence or level of lactoferrin in a sample isalso useful in the present invention. In certain instances, the presenceor level of lactoferrin is detected at the level of mRNA expression withan assay such as, for example, a hybridization assay or anamplification-based assay. In certain other instances, the presence orlevel of lactoferrin is detected at the level of protein expressionusing, for example, an immunoassay (e.g., ELISA) or animmunohistochemical assay. A lactoferrin ELISA kit available fromCalbiochem (San Diego, Calif.) can be used to detect human lactoferrinin a plasma, urine, bronchoalveolar lavage, or cerebrospinal fluidsample. Similarly, an ELISA kit available from U.S. Biological(Swampscott, Mass.) can be used to determine the level of lactoferrin ina plasma sample. U.S. Patent Publication No. 20040137536 describes anELISA assay for determining the presence of elevated lactoferrin levelsin a stool sample. Likewise, U.S. Patent Publication No. 20040033537describes an ELISA assay for determining the concentration of endogenouslactoferrin in a stool, mucus, or bile sample. In some embodiments, thenpresence or level of anti-lactoferrin antibodies can be detected in asample using, e.g., lactoferrin protein or a fragment thereof.

The determination of the presence or level of one or more pyruvatekinase isozymes such as M1-PK and M2-PK in a sample is also useful inthe present invention. In certain instances, the presence or level ofM1-PK and/or M2-PK is detected at the level of mRNA expression with anassay such as, for example, a hybridization assay or anamplification-based assay. In certain other instances, the presence orlevel of M1-PK and/or M2-PK is detected at the level of proteinexpression using, for example, an immunoassay (e.g., ELISA) or animmunohistochemical assay. Pyruvate kinase isozymes M1/M2 are also knownas pyruvate kinase muscle isozyme (PKM), pyruvate kinase type K,cytosolic thyroid hormone-binding protein (CTHBP), thyroidhormone-binding protein 1 (THBP1), or opa-interacting protein 3 (OIP3).

In further embodiments, the determination of the presence or level ofone or more growth factors in a sample is also useful in the presentinvention. Non-limiting examples of growth factors include transforminggrowth factors (TGF) such as TGF-α, TGF-β, TGF-β2, TGF-β3, etc., whichare described in detail below.

6. Exemplary Set of Inflammatory Markers

In particular embodiments, at least one or a plurality (e.g., two,three, four, five, six, seven, or all eight, such as, e.g., a panel oran array) of the following inflammatory markers can be detected (e.g.,alone or in combination with biomarkers from other categories) to aid orassist in predicting disease course, and/or to improve the accuracy ofselecting therapy, optimizing therapy, reducing toxicity, and/ormonitoring the efficacy of therapeutic treatment to anti-TNF drugtherapy: (1) GM-CSF; (2) IFN-γ; (3) IL-1β; (4) IL-2; (5) IL-6; (6) IL-8;(7) TNF-α; and (8) sTNF RII.

C. Anti-Inflammatory Markers

In certain embodiments, a variety of anti-inflammatory markers areparticularly useful in the methods of the present invention forpersonalized therapeutic management by selecting therapy, optimizingtherapy, reducing toxicity, and/or monitoring the efficacy oftherapeutic treatment with one or more therapeutic agents such asbiologics (e.g., anti-TNF drugs). In particular embodiments, the methodsdescribed herein utilize the determination of a disease activity profile(DAP) based upon one or more (a plurality of) anti-inflammatory markers(e.g., alone or in combination with biomarkers from other categories) toaid or assist in predicting disease course, selecting an appropriateanti-TNF drug therapy, optimizing anti-TNF drug therapy, reducingtoxicity associated with anti-TNF drug therapy, and/or monitoring theefficacy of therapeutic treatment with an anti-TNF drug.

Non-limiting examples of anti-inflammatory markers include IL-12p70 andIL-10. In preferred embodiments, the presence and/or concentrationlevels of both IL-12p70 and IL-10 are determined.

In certain instances, the presence or level of a particularanti-inflammatory marker is detected at the level of mRNA expressionwith an assay such as, for example, a hybridization assay or anamplification-based assay. In certain other instances, the presence orlevel of a particular anti-inflammatory marker is detected at the levelof protein expression using, for example, an immunoassay (e.g., ELISA)or an immunohistochemical assay.

The human IL-12p70 polypeptide is a heterodimer made up of two subunitsof IL-12 proteins: one is 40 kDa (IL-12p40) and one is 35 kDa(IL-12p35). Suitable ELISA kits for determining the presence or level ofIL-12p70 in a serum, plasma, saliva, or urine sample are available from,e.g., Gen-Probe Diaclone SAS (France), Abazyme (Needham, Mass.), BDBiosciences Pharmingen (San Diego, Calif.), Cell Sciences (Canton,Mass.), eBioscience (San Diego, Calif.), Invitrogen (Camarillo, Calif.),R&D Systems, Inc. (Minneapolis, Minn.), and Thermo Scientific PierceProtein Research Products (Rockford, Ill.).

The human IL-10 polypeptide is an anti-inflammatory cytokine that isalso known as human cytokine synthesis inhibitory factor (CSIF).Suitable ELISA kits for determining the presence or level of IL-12p70 ina serum, plasma, saliva, or urine sample are available from, e.g.,Antigenix America Inc. (Huntington Station, N.Y.), BD BiosciencesPharmingen (San Diego, Calif.), Cell Sciences (Canton, Mass.),eBioscience (San Diego, Calif.), Gen-Probe Diaclone SAS (France),Invitrogen (Camarillo, Calif.), R&D Systems, Inc. (Minneapolis, Minn.),and Thermo Scientific Pierce Protein Research Products (Rockford, Ill.).

D. Serology (Immune Markers)

The determination of serological or immune markers such asautoantibodies in a sample (e.g., serum sample) is also useful in thepresent invention. Antibodies against anti-inflammatory molecules suchas IL-10, TGF-β, and others might suppress the body's ability to controlinflammation and the presence or level of these antibodies in thepatient indicates the use of powerful immunosuppressive medications suchas anti-TNF drugs. Mucosal healing might result in a decrease in theantibody titre of antibodies to bacterial antigens such as, e.g., OmpC,flagellins (cBir-1, Fla-A, Fla-X, etc.), I2, and others (pANCA, ASCA,etc.).

As such, in certain aspects, the methods described herein utilize thedetermination of a disease activity profile (DAP) based upon one or more(a plurality of) serological or immune markers (e.g., alone or incombination with biomarkers from other categories) to aid or assist inpredicting disease course, selecting an appropriate anti-TNF drugtherapy, optimizing anti-TNF drug therapy, reducing toxicity associatedwith anti-TNF drug therapy, and/or monitoring the efficacy oftherapeutic treatment with an anti-TNF drug.

Non-limiting examples of serological immune markers suitable for use inthe present invention include anti-neutrophil antibodies,anti-Saccharomyces cerevisiae antibodies, and/or other anti-microbialantibodies.

1. Anti-Neutrophil Antibodies

The determination of ANCA levels and/or the presence or absence of pANCAin a sample is useful in the methods of the present invention. As usedherein, the term “anti-neutrophil cytoplasmic antibody” or “ANCA”includes antibodies directed to cytoplasmic and/or nuclear components ofneutrophils. ANCA activity can be divided into several broad categoriesbased upon the ANCA staining pattern in neutrophils: (1) cytoplasmicneutrophil staining without perinuclear highlighting (cANCA); (2)perinuclear staining around the outside edge of the nucleus (pANCA); (3)perinuclear staining around the inside edge of the nucleus (NSNA); and(4) diffuse staining with speckling across the entire neutrophil(SAPPA). In certain instances, pANCA staining is sensitive to DNasetreatment. The term ANCA encompasses all varieties of anti-neutrophilreactivity, including, but not limited to, cANCA, pANCA, NSNA, andSAPPA. Similarly, the term ANCA encompasses all immunoglobulin isotypesincluding, without limitation, immunoglobulin A and G.

ANCA levels in a sample from an individual can be determined, forexample, using an immunoassay such as an enzyme-linked immunosorbentassay (ELISA) with alcohol-fixed neutrophils. The presence or absence ofa particular category of ANCA such as pANCA can be determined, forexample, using an immunohistochemical assay such as an indirectfluorescent antibody (IFA) assay. Preferably, the presence or absence ofpANCA in a sample is determined using an immunofluorescence assay withDNase-treated, fixed neutrophils. In addition to fixed neutrophils,antigens specific for ANCA that are suitable for determining ANCA levelsinclude, without limitation, unpurified or partially purified neutrophilextracts; purified proteins, protein fragments, or synthetic peptidessuch as histone H1 or ANCA-reactive fragments thereof (see, e.g., U.S.Pat. No. 6,074,835); histone H1-like antigens, porin antigens,Bacteroides antigens, or ANCA-reactive fragments thereof (see, e.g.,U.S. Pat. No. 6,033,864); secretory vesicle antigens or ANCA-reactivefragments thereof (see, e.g., U.S. patent application Ser. No.08/804,106); and anti-ANCA idiotypic antibodies. One skilled in the artwill appreciate that the use of additional antigens specific for ANCA iswithin the scope of the present invention.

2. Anti-Saccharomyces cerevisiae Antibodies

The determination of ASCA (e.g., ASCA-IgA and/or ASCA-IgG) levels in asample is useful in the present invention. As used herein, the term“anti-Saccharomyces cerevisiae immunoglobulin A” or “ASCA-IgA” includesantibodies of the immunoglobulin A isotype that react specifically withS. cerevisiae. Similarly, the term “anti-Saccharomyces cerevisiaeimmunoglobulin G” or “ASCA-IgG” includes antibodies of theimmunoglobulin G isotype that react specifically with S. cerevisiae.

The determination of whether a sample is positive for ASCA-IgA orASCA-IgG is made using an antigen specific for ASCA. Such an antigen canbe any antigen or mixture of antigens that is bound specifically byASCA-IgA and/or ASCA-IgG. Although ASCA antibodies were initiallycharacterized by their ability to bind S. cerevisiae, those of skill inthe art will understand that an antigen that is bound specifically byASCA can be obtained from S. cerevisiae or from a variety of othersources so long as the antigen is capable of binding specifically toASCA antibodies. Accordingly, exemplary sources of an antigen specificfor ASCA, which can be used to determine the levels of ASCA-IgA and/orASCA-IgG in a sample, include, without limitation, whole killed yeastcells such as Saccharomyces or Candida cells; yeast cell wall mannansuch as phosphopeptidomannan (PPM); oligosachharides such asoligomannosides; neoglycolipids; anti-ASCA idiotypic antibodies; and thelike. Different species and strains of yeast, such as S. cerevisiaestrain Su1, Su2, CBS 1315, or BM 156, or Candida albicans strain VW32,are suitable for use as an antigen specific for ASCA-IgA and/orASCA-IgG. Purified and synthetic antigens specific for ASCA are alsosuitable for use in determining the levels of ASCA-IgA and/or ASCA-IgGin a sample. Examples of purified antigens include, without limitation,purified oligosaccharide antigens such as oligomannosides. Examples ofsynthetic antigens include, without limitation, syntheticoligomannosides such as those described in U.S. Patent Publication No.20030105060, e.g., D-Man β(1-2) D-Man β(1-2) D-Man β(1-2) D-Man-OR,D-Man α(1-2) D-Man α(1-2) D-Man α(1-2) D-Man-OR, and D-Man α(1-3) D-Manα(1-2) D-Man α(1-2) D-Man-OR, wherein R is a hydrogen atom, a C₁ to C₂₀alkyl, or an optionally labeled connector group.

Preparations of yeast cell wall mannans, e.g., PPM, can be used indetermining the levels of ASCA-IgA and/or ASCA-IgG in a sample. Suchwater-soluble surface antigens can be prepared by any appropriateextraction technique known in the art, including, for example, byautoclaving, or can be obtained commercially (see, e.g., Lindberg etal., Gut, 33:909-913 (1992)). The acid-stable fraction of PPM is alsouseful in the present invention (Sendid et al., Clin. Diag. Lab.Immunol., 3:219-226 (1996)). An exemplary PPM that is useful indetermining ASCA levels in a sample is derived from S. uvarum strainATCC #38926.

Purified oligosaccharide antigens such as oligomannosides can also beuseful in determining the levels of ASCA-IgA and/or ASCA-IgG in asample. The purified oligomannoside antigens are preferably convertedinto neoglycolipids as described in, for example, Faille et al., Eur. J.Microbiol. Infect. Dis., 11:438-446 (1992). One skilled in the artunderstands that the reactivity of such an oligomannoside antigen withASCA can be optimized by varying the mannosyl chain length (Frosh etal., Proc Natl. Acad. Sci. USA, 82:1194-1198 (1985)); the anomericconfiguration (Fukazawa et al., In “Immunology of Fungal Disease,” E.Kurstak (ed.), Marcel Dekker Inc., New York, pp. 37-62 (1989); Nishikawaet al., Microbiol. Immunol., 34:825-840 (1990); Poulain et al., Eur. J.Clin. Microbiol., 23:46-52 (1993); Shibata et al., Arch. Biochem.Biophys., 243:338-348 (1985); Trinel et al., Infect. Immun.,60:3845-3851 (1992)); or the position of the linkage (Kikuchi et al.,Planta, 190:525-535 (1993)).

Suitable oligomannosides for use in the methods of the present inventioninclude, without limitation, an oligomannoside having the mannotetraoseMan(1-3) Man(1-2) Man(1-2) Man. Such an oligomannoside can be purifiedfrom PPM as described in, e.g., Faille et al., supra. An exemplaryneoglycolipid specific for ASCA can be constructed by releasing theoligomannoside from its respective PPM and subsequently coupling thereleased oligomannoside to 4-hexadecylaniline or the like.

3. Anti-Microbial Antibodies

The determination of anti-OmpC antibody levels in a sample is alsouseful in the present invention. As used herein, the term “anti-outermembrane protein C antibody” or “anti-OmpC antibody” includes antibodiesdirected to a bacterial outer membrane porin as described in, e.g., PCTPatent Publication No. WO 01/89361. The term “outer membrane protein C”or “OmpC” refers to a bacterial porin that is immunoreactive with ananti-OmpC antibody.

The level of anti-OmpC antibody present in a sample from an individualcan be determined using an OmpC protein or a fragment thereof such as animmunoreactive fragment thereof. Suitable OmpC antigens useful indetermining anti-OmpC antibody levels in a sample include, withoutlimitation, an OmpC protein, an OmpC polypeptide having substantiallythe same amino acid sequence as the OmpC protein, or a fragment thereofsuch as an immunoreactive fragment thereof. As used herein, an OmpCpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with an OmpC protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as E. coli, by recombinant expression of anucleic acid such as Genbank Accession No. K00541, by synthetic meanssuch as solution or solid phase peptide synthesis, or by using phagedisplay.

The determination of anti-I2 antibody levels in a sample is also usefulin the present invention. As used herein, the term “anti-I2 antibody”includes antibodies directed to a microbial antigen sharing homology tobacterial transcriptional regulators as described in, e.g., U.S. Pat.No. 6,309,643. The term “I2” refers to a microbial antigen that isimmunoreactive with an anti-I2 antibody. The microbial I2 protein is apolypeptide of 100 amino acids sharing some similarity weak homologywith the predicted protein 4 from C. pasteurianum, Rv3557c fromMycobacterium tuberculosis, and a transcriptional regulator from Aquifexaeolicus. The nucleic acid and protein sequences for the I2 protein aredescribed in, e.g., U.S. Pat. No. 6,309,643.

The level of anti-I2 antibody present in a sample from an individual canbe determined using an I2 protein or a fragment thereof such as animmunoreactive fragment thereof. Suitable I2 antigens useful indetermining anti-I2 antibody levels in a sample include, withoutlimitation, an I2 protein, an I2 polypeptide having substantially thesame amino acid sequence as the I2 protein, or a fragment thereof suchas an immunoreactive fragment thereof. Such I2 polypeptides exhibitgreater sequence similarity to the I2 protein than to the C.pasteurianum protein 4 and include isotype variants and homologsthereof. As used herein, an I2 polypeptide generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with a naturally-occurring I2 protein, with the amino acididentity determined using a sequence alignment program such as CLUSTALW.Such I2 antigens can be prepared, for example, by purification frommicrobes, by recombinant expression of a nucleic acid encoding an I2antigen, by synthetic means such as solution or solid phase peptidesynthesis, or by using phage display.

The determination of anti-flagellin antibody levels in a sample is alsouseful in the present invention. As used herein, the term“anti-flagellin antibody” includes antibodies directed to a proteincomponent of bacterial flagella as described in, e.g., PCT PatentPublication No. WO 03/053220 and U.S. Patent Publication No.20040043931. The term “flagellin” refers to a bacterial flagellumprotein that is immunoreactive with an anti-flagellin antibody.Microbial flagellins are proteins found in bacterial flagellum thatarrange themselves in a hollow cylinder to form the filament.

The level of anti-flagellin antibody present in a sample from anindividual can be determined using a flagellin protein or a fragmentthereof such as an immunoreactive fragment thereof. Suitable flagellinantigens useful in determining anti-flagellin antibody levels in asample include, without limitation, a flagellin protein such as Cbir-1flagellin, flagellin X, flagellin A, flagellin B, fragments thereof, andcombinations thereof, a flagellin polypeptide having substantially thesame amino acid sequence as the flagellin protein, or a fragment thereofsuch as an immunoreactive fragment thereof. As used herein, a flagellinpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a naturally-occurringflagellin protein, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such flagellin antigens canbe prepared, e.g., by purification from bacterium such as HelicobacterBilis, Helicobacter mustelae, Helicobacter pylori, Butyrivibriofibrisolvens, and bacterium found in the cecum, by recombinantexpression of a nucleic acid encoding a flagellin antigen, by syntheticmeans such as solution or solid phase peptide synthesis, or by usingphage display.

E. Cell Surface Receptors

The determination of cell surface receptors in a sample is also usefulin the present invention. The half-life of anti-TNF drugs such asRemicade and Humira is significantly decreased in patients with a highlevel of inflammation. CD64, the high-affinity receptor forimmunoglobulin (Ig) G1 and IgG3, is predominantly expressed bymononuclear phagocytes. Resting polymorphonuclear (PMN) cells scarcelyexpress CD64, but the expression of this marker is upregulated byinterferon and granulocyte-colony-stimulating factor acting on myeloidprecursors in the bone marrow. Crosslinking of CD64 with IgG complexesexerts a number of cellular responses, including the internalization ofimmune complexes by endocytosis, phagocytosis of opsonized particles,degranulation, activation of the oxidative burst, and the release ofcytokines.

As such, in certain aspects, the methods described herein utilize thedetermination of a disease activity profile (DAP) based upon one or more(a plurality of) cell surface receptors such as CD64 (e.g., alone or incombination with biomarkers from other categories) to aid or assist inpredicting disease course, selecting an appropriate anti-TNF drugtherapy, optimizing anti-TNF drug therapy, reducing toxicity associatedwith anti-TNF drug therapy, and/or monitoring the efficacy oftherapeutic treatment with an anti-TNF drug.

F. Signaling Pathways

The determination of signaling pathways in a sample is also useful inthe present invention. Polymorphonuclear (PMN) cell activation, followedby infiltration into the intestinal mucosa (synovium for RA) andmigration across the crypt epithelium is regarded as a key feature ofIBD. It has been estimated by fecal indium-111-labeled leukocyteexcretion that migration of PMN cells from the circulation to thediseased section of the intestine is increased by 10-fold or more in IBDpatients. Thus, measuring activation of PMN cells from blood or tissueinflammation by measuring signaling pathways using an assay such as theCollaborative Enzyme Enhanced Reactive ImmunoAssay (CEER) describedherein is an ideal way to understand inflammatory disease.

As such, in certain aspects, the methods described herein utilize thedetermination of a disease activity profile (DAP) based upon one or more(a plurality of) signal transduction molecules in one or more signalingpathways (e.g., alone or in combination with biomarkers from othercategories) to aid or assist in predicting disease course, selecting anappropriate anti-TNF drug therapy, optimizing anti-TNF drug therapy,reducing toxicity associated with anti-TNF drug therapy, and/ormonitoring the efficacy of therapeutic treatment with an anti-TNF drug.In preferred embodiments, the total (e.g., expression) level and/oractivation (e.g., phosphorylation) level of one or more signaltransduction molecules in one or more signaling pathways is measured.

The term “signal transduction molecule” or “signal transducer” includesproteins and other molecules that carry out the process by which a cellconverts an extracellular signal or stimulus into a response, typicallyinvolving ordered sequences of biochemical reactions inside the cell.Examples of signal transduction molecules include, but are not limitedto, receptor tyrosine kinases such as EGFR (e.g., EGFR/HER1/ErbB1,HER2/Neu/ErbB2, HER3/ErbB3, HER4/ErbB4), VEGFR1/FLT1, VEGFR2/FLK1/KDR,VEGFR3/FLT4, FLT3/FLK2, PDGFR (e.g., PDGFRA, PDGFRB), c-KIT/SCFR, INSR(insulin receptor), IGF-IR, IGF-IIR, IRR (insulin receptor-relatedreceptor), CSF-1R, FGFR 1-4, HGFR 1-2, CCK4, TRK A-C, c-MET, RON, EPHA1-8, EPHB 1-6, AXL, MER, TYRO3, TIE 1-2, TEK, RYK, DDR 1-2, RET, c-ROS,V-cadherin, LTK (leukocyte tyrosine kinase), ALK (anaplastic lymphomakinase), ROR 1-2, MUSK, AATYK 1-3, and RTK 106; truncated forms ofreceptor tyrosine kinases such as truncated HER2 receptors with missingamino-terminal extracellular domains (e.g., p95ErbB2 (p95m), p110, p95c,p95n, etc.), truncated cMET receptors with missing amino-terminalextracellular domains, and truncated HER3 receptors with missingamino-terminal extracellular domains; receptor tyrosine kinase dimers(e.g., p95HER2/HER3; p95HER2/HER2; truncated HER3 receptor with HER1,HER2, HER3, or HER4; HER2/HER2; HER3/HER3; HER2/HER3; HER1/HER2;HER1/HER3; HER2/HER4; HER3/HER4; etc.); non-receptor tyrosine kinasessuch as BCR-ABL, Src, Frk, Btk, Csk, Abl, Zap70, Fes/Fps, Fak, Jak, Ack,and LIMK; tyrosine kinase signaling cascade components such as AKT(e.g., AKT1, AKT2, AKT3), MEK (MAP2K1), ERK2 (MAPK1), ERK1 (MAPK3), PI3K(e.g., PIK3CA (p110), PIK3R1 (p85)), PDK1, PDK2, phosphatase and tensinhomolog (PTEN), SGK3, 4E-BP1, P70S6K (e.g., p70 S6 kinase splice variantalpha 1), protein tyrosine phosphatases (e.g., PTP1B, PTPN13, BDP1,etc.), RAF, PLA2, MEKK, JNKK, JNK, p38, She (p66), Ras (e.g., K-Ras,N-Ras, H-Ras), Rho, Rac1, Cdc42, PLC, PKC, p53, cyclin D1, STAT1, STAT3,phosphatidylinositol 4,5-bisphosphate (PIP2), phosphatidylinositol3,4,5-trisphosphate (PIP3), mTOR, BAD, p21, p27, ROCK, IP3, TSP-1, NOS,GSK-3β, RSK 1-3, JNK, c-Jun, Rb, CREB, Ki67, paxillin, NF-kB, and IKK;nuclear hormone receptors such as estrogen receptor (ER), progesteronereceptor (PR), androgen receptor, glucocorticoid receptor,mineralocorticoid receptor, vitamin A receptor, vitamin D receptor,retinoid receptor, thyroid hormone receptor, and orphan receptors;nuclear receptor coactivators and repressors such as amplified in breastcancer-1 (AIB 1) and nuclear receptor corepressor 1 (NCOR),respectively; and combinations thereof.

The term “activation state” refers to whether a particular signaltransduction molecule is activated. Similarly, the term “activationlevel” refers to what extent a particular signal transduction moleculeis activated. The activation state typically corresponds to thephosphorylation, ubiquitination, and/or complexation status of one ormore signal transduction molecules. Non-limiting examples of activationstates (listed in parentheses) include: HER1/EGFR (EGFRvIII,phosphorylated (p-) EGFR, EGFR:Shc, ubiquitinated (u-) EGFR,p-EGFRvIII); ErbB2 (p-ErbB2, p95HER2 (truncated ErbB2), p-p95HER2,ErbB2:Shc, ErbB2:PI3K, ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4); ErbB3(p-ErbB3, truncated ErbB3, ErbB3:PI3K, p-ErbB3:PI3K, ErbB3:Shc); ErbB4(p-ErbB4, ErbB4:Shc); c-MET (p-c-MET, truncated c-MET, c-Met:HGFcomplex); AKT1 (p-AKT1); AKT2 (p-AKT2); AKT3 (p-AKT3); PTEN (p-PTEN);P70S6K (p-P70S6K); MEK (p-MEK); ERK1 (p-ERK1); ERK2 (p-ERK2); PDK1(p-PDK1); PDK2 (p-PDK2); SGK3 (p-SGK3); 4E-BP1 (p-4E-BP1); PIK3R1(p-PIK3R1); c-KIT (p-c-KIT); ER (p-ER); IGF-1R (p-IGF-1R, IGF-1R:IRS,IRS:PI3K, p-IRS, IGF-1R:PI3K); INSR (p-INSR); FLT3 (p-FLT3); HGFR1(p-HGFR1); HGFR2 (p-HGFR2); RET (p-RET); PDGFRA (p-PDGFRA); PDGFRB(p-PDGFRB); VEGFR1 (p-VEGFR1, VEGFR1:PLCγ, VEGFR1:Src); VEGFR2(p-VEGFR2, VEGFR2:PLCγ, VEGFR2:Src, VEGFR2:heparin sulphate,VEGFR2:VE-cadherin); VEGFR3 (p-VEGFR3); FGFR1 (p-FGFR1); FGFR2(p-FGFR2); FGFR3 (p-FGFR3); FGFR4 (p-FGFR4); TIE1 (p-TIE1); TIE2(p-TIE2); EPHA (p-EPHA); EPHB (p-EPHB); GSK-3β (p-GSK-3β); NF-kB(p-NF-kB, NF-kB-IkB alpha complex and others), IkB (p-IkB, p-P65:IkB);IKK (phospho IKK); BAD (p-BAD, BAD:14-3-3); mTOR (p-mTOR); Rsk-1(p-Rsk-1); Jnk (p-Jnk); P38 (p-P38); STAT1 (p-STAT1); STAT3 (p-STAT3);FAK (p-FAK); RB (p-RB); Ki67; p53 (p-p53); CREB (p-CREB); c-Jun(p-c-Jun); c-Src (p-c-Src); paxillin (p-paxillin); GRB2 (p-GRB2), She(p-Shc), Ras (p-Ras), GAB1 (p-GAB1), SHP2 (p-SHP2), GRB2 (p-GRB2), CRKL(p-CRKL), PLCγ (p-PLCγ), PKC (e.g., p-PKCα, p-PKCβ, p-PKCδ), adducin(p-adducin), RB1 (p-RB1), and PYK2 (p-PYK2).

The following tables provide additional examples of signal transductionmolecules for which total levels and/or activation (e.g.,phosphorylation) levels can be determined in a sample (e.g., alone or incombination with biomarkers from other categories) to aid or assist inpredicting disease course, selecting an appropriate anti-TNF drugtherapy, optimizing anti-TNF drug therapy, reducing toxicity associatedwith anti-TNF drug therapy, or monitoring the efficacy of therapeutictreatment with an anti-TNF drug.

Phospho Total/Phospho Assays sites VEGFR2 Total VEGFR2 Phospho Y951,1212 Erk Total Erk Phospho T202/Y204 Akt Total Akt Phospho T308, S473MEK Total MEK Phospho S217/221 MEK Total MEK Phospho S217/221 P70S6KTotal P70S6K Phospho T389(T229) PTEN Total VEGFR1(T) VEGFR1 Phospho SGKtotal SGK phospho T320, S486 CRKL Total CRKL Phospho Y207 SRC Total SRCPhospho Y416, 527 FAK Total FAK Phospho Y397 BCR Total BCR Phospho PI3Kactivated PI3K complexed P85 Y688 4EBP1 4EBP1 phospho T70, T37, T46PRAS40 PRAS40 phospho T246 TIE Total TIE-2 Phospho Y992(S1119) Jak 2Total JAK 2 Phospho Y1007/1008 STAT5 Total STAT 5 Phospho Y694/699 STAT3 Total STAT 3 Phospho Y705 FGFR1 total FGFR1 Phospho Y653, 766 FGFR2total FGFR 2 Phospho Y653 FGFR3 total FGFR 3 Phospho FGFR4 total FGFR 4Phospho Axl total Axl Phospho Y702 BAD total BAD Phospho (S112)(S136)RSK total RSK Phospho (T359/S363) PDK total PDK 1 Phospho (S241) JAK 1and 3 total JAK 1 and 3 Phospho TSC2 total TSC 2 Phospho S664, S939 S6RPTotal S6RP phospho S235/236

The Collaborative Enzyme Enhanced Reactive ImmunoAssay (CEER), alsoknown as the Collaborative Proximity Immunoassay (COPIA), is describedin the following patent documents which are herein incorporated byreference in their entirety for all purposes: PCT Publication No. WO2008/036802; PCT Publication No. WO 2009/012140; PCT Publication No. WO2009/108637; PCT Publication No. WO 2010/132723; PCT Publication No. WO2011/008990; and PCT Application No. PCT/US2010/053386, filed Oct. 20,2010.

G. Elimination Rate Constant

In certain embodiments, a marker for the disease activity profile (DAP)is kel, or the elimination rate constant of an antibody such as ananti-TNF antibody (e.g., infliximab). The determination of anelimination rate constant such as kel is particularly useful in themethods of the invention for personalized therapeutic management byselecting therapy, optimizing therapy, reducing toxicity, and/ormonitoring the efficacy of therapeutic treatment with one or moretherapeutic agents such as biologics (e.g., anti-TNF drugs).

In certain instances, a differential equation can be used to model drugelimination from the patient. In certain instances, a two-compartment PKmodel can be used. In this instance, the equation for the drug in thecentral compartment following intravenous bolus administration is:

$\frac{d\; X\; 1}{d\; t} = {{{{- {kel}} \cdot X}\; 1} - {k\;{12 \cdot X}\; 1} + {k\;{21 \cdot X}\; 2.}}$

The kel•X1 term describes elimination of the drug from the centralcompartment, while the k12•X1 and k21•X2 terms describe the distributionof drug between the central and peripheral compartments.

H. Genetic Markers

The determination of the presence or absence of allelic variants (e.g.,SNPs) in one or more genetic markers in a sample (e.g., alone or incombination with biomarkers from other categories) is also useful in themethods of the present invention to aid or assist in predicting diseasecourse, selecting an appropriate anti-TNF drug therapy, optimizinganti-TNF drug therapy, reducing toxicity associated with anti-TNF drugtherapy, or monitoring the efficacy of therapeutic treatment with ananti-TNF drug.

Non-limiting examples of genetic markers include, but are not limitedto, any of the inflammatory pathway genes and corresponding SNPs thatcan be genotyped as set forth in Table 1 (e.g., a NOD2/CARD15 gene, anIL12/IL23 pathway gene, etc.). Preferably, the presence or absence of atleast one allelic variant, e.g., a single nucleotide polymorphism (SNP),in the NOD2/CARD15 gene and/or one or more genes in the IL12/IL23pathway is determined. See, e.g., Barrett et al., Nat. Genet., 40:955-62(2008) and Wang et al., Amer. J. Hum. Genet., 84:399-405 (2009).

TABLE 1 Gene SNP NOD2 (R702W)-SNP8 rs2066844 NOD2 (G908R)-SNP12rs2066845 NOD2 (3020insC)-SNP13 rs5743293 ATG16L1 (T300A) rs2241880IL23R (R381Q) rs11209026 DLG5 rs2165047 NOD2/CARD15 rs2066847 IL23Rrs11465804 ATG16L1 rs3828309 MST1 rs3197999 PTGER4 rs4613763 IRGMrs11747270 TNFSF15 rs4263839 ZNF365 rs10995271 NKX2-3 rs11190140 PTPN2rs2542151 PTPN22 rs2476601 ITLN1 rs2274910 IL12B rs10045431 CDKAL1rs6908425 CCR6 rs2301436 JAK2 rs10758669 C11orf30 rs7927894 LRRK2, MUC19rs11175593 ORMDL3 rs2872507 STAT3 rs744166 ICOSLG rs762421 GCKR rs780094BTNL2, SLC26A3, HLA-DRB1, rs3763313 HLA-DQA1 PUS10 rs13003464 CCL2, CCL7rs991804 LYRM4 rs12529198 SLC22A23 rs17309827 IL18RAP rs917997 IL12RB2rs7546245 IL12RB1 rs374326 CD3D rs3212262 CD3G rs3212262 CD247 rs704853JUN rs6661505 CD3E rs7937334 IL18R1 rs1035127 CCR5 MAPK14 rs2237093 IL18rs11214108 IFNG rs10878698 MAP2K6 rs2905443 STAT4 rs1584945 IL12Ars6800657 TYK2 rs12720356 ETV5 rs9867846 MAPK8 rs17697885 IRGMrs13361189 IRGM rs4958847 IRGM rs1000113 IRGM rs11747270 TL1A/TNFSF15rs6478109 TL1A/TNFSF15 rs6478108 TL1A/TNFSF15 rs4263839 PTN22 rs2476601CCR6 rs1456893 CCR6 rs2301436 5p13/PTGER4 rs1373692 5p13/PTGER4rs4495224 5p13/PTGER4 rs7720838 5p13/PTGER4 rs4613763 ITLN1 rs2274910ITLN1 rs9286879 ITLN1 rs11584383 IBD5/5q31 rs2188962 IBD5/5q31 rs252057IBD5/5q31 rs10067603 GCKR rs780094 TNFRSF6B rs1736135 ZNF365 rs224136ZNF365 rs10995271 C11orf30 rs7927894 LRRK2; MUC19 rs1175593 IL-27rs8049439 TLR2 rs4696480 TLR2 rs3804099 TLR2 rs3804100 TLR2 rs5743704TLR2 rs2405432 TLR4 (D299G) rs4986790 TLR4 (T399I) rs4986791 TLR4(S360N) rs4987233 TLR9 rs187084 TLR9 rs352140 NFC4 rs4821544 KIF21Brs11584383 IKZF1 rs1456893 C11orf30 rs7927894 CCL2, CCL7 rs991804 ICOSLGrs762421 TNFAIP3 rs7753394 FLJ45139 rs2836754 PTGER4 rs4613763 ECM1rs7511649 ECM1 (T130M) rs3737240 ECM1 (G290S) rs13294 GLI1 (G933D)rs2228224 GLI1 (Q1100E) rs2228226 MDR1 (3435C > T) rs1045642 MDR1(A893S/T) rs2032582 MAGI2 rs6962966 MAGI2 rs2160322 IL26 rs12815372IFNG, IL26 rs1558744 IFNG, IL26 rs971545 IL26 rs2870946 ARPC2 rs12612347IL10, IL19 rs3024493 IL10, IL19 rs3024505 IL23R rs1004819 IL23Rrs2201841 IL23R rs11465804 IL23R rs10889677 BTLN2 rs9268480 HLA-DRB1rs660895 MEP1 rs6920863 MEP1 rs2274658 MEP1 rs4714952 MEP1 rs1059276PUS10 rs13003464 PUS10 rs6706689 RNF186 rs3806308 RNF186 rs1317209RNF186 rs6426833 FCGR2A, C rs10800309 CEP72 rs4957048 DLD, LAMB1rs4598195 CAPN10, KIF1A rs4676410 IL23R rs11805303 IL23R rs7517847IL12B/p40 rs1368438 IL12B/p40 rs10045431 IL12B/p40 rs6556416 IL12B/p40rs6887695 IL12B/p40 rs3212227 STAT3 rs744166 JAK2 rs10974914 JAK2rs10758669 NKX2-3 rs6584283 NKX2-3 rs10883365 NKX2-3 rs11190140 IL18RAPrs917997 LYRM4 rs12529198 CDKAL1 rs6908425 MAGI2 rs2160322 TNFRSF6Brs2160322 TNFRSF6B rs2315008 TNFRSF6B rs4809330 PSMG1 rs2094871 PSMG1rs2836878 PTPN2 rs2542151 MST1/3p21 rs9858542 MST1/3p21 rs3197999SLC22A23 rs17309827 MHC rs660895 XBP1 rs35873774 ICOSLG1 rs762421 BTLN2rs3763313 BTLN2 rs2395185 BTLN2 rs9268480 ATG5 rs7746082 CUL2, CREMrs17582416 CARD9 rs4077515 ORMDL3 rs2872507 ORMDL3 rs2305480

Additional SNPs useful in the present invention include, e.g.,rs2188962, rs9286879, rs11584383, rs7746082, rs1456893, rs1551398,rs17582416, rs3764147, rs1736135, rs4807569, rs7758080, and rs8098673.See, e.g., Barrett et al., Nat. Genet., 40:955-62 (2008).

In particular embodiments, the presence or absence of one or moremutations in one or more of the following genetic markers is determined:inflammatory pathway genes, e.g., the presence or absence of variantalleles (e.g., SNPs) in one or more inflammatory markers such as, e.g.,NOD2/CARD15 (e.g., SNP 8, SNP 12, and/or SNP 13 described in U.S. Pat.No. 7,592,437), ATG16L1 (e.g., the rs2241880 (T300A) SNP described inLakatos et al., Digestive and Liver Disease, 40 (2008) 867-873), IL23R(e.g., the rs11209026 (R381Q) SNP described in Lakatos et al.), thehuman leukocyte antigen (HLA) genes and/or cytokine genes described in,e.g., Gasche et al. (Eur. J. Gastroenterology & Hepatology, (2003)15:599-606), and the DLG5 and/or OCTN genes from the IBD5 locus.

1. NOD2/CARD15

The determination of the presence or absence of allelic variants such asSNPs in the NOD2/CARD15 gene is particularly useful in the presentinvention. As used herein, the term “NOD2/CARD15 variant” or “NOD2variant” includes a nucleotide sequence of a NOD2 gene containing one ormore changes as compared to the wild-type NOD2 gene or an amino acidsequence of a NOD2 polypeptide containing one or more changes ascompared to the wild-type NOD2 polypeptide sequence. NOD2, also known asCARD15, has been localized to the IBD1 locus on chromosome 16 andidentified by positional-cloning (Hugot et al., Nature, 411:599-603(2001)) as well as a positional candidate gene strategy (Ogura et al.,Nature, 411:603-606 (2001); Hampe et al., Lancet, 357:1925-1928 (2001)).The IBD1 locus has a high multipoint linkage score (MLS) forinflammatory bowel disease (MLS=5.7 at marker D16S411 in 16q12). See,e.g., Cho et al., Inflamm. Bowel Dis., 3:186-190 (1997); Akolkar et al.,Am. J. Gastroenterol., 96:1127-1132 (2001); Ohmen et al., Hum. Mol.Genet., 5:1679-1683 (1996); Parkes et al., Lancet, 348:1588 (1996);Cavanaugh et al., Ann. Hum. Genet., 62:291-8 (1998); Brant et al.,Gastroenterology, 115:1056-1061 (1998); Curran et al., Gastroenterology,115:1066-1071 (1998); Hampe et al., Am. J. Hum. Genet., 64:808-816(1999); and Annese et al., Eur. J. Hum. Genet., 7:567-573 (1999).

The mRNA (coding) and polypeptide sequences of human NOD2 are set forthin, e.g., Genbank Accession Nos. NM_022162 and NP_071445, respectively.In addition, the complete sequence of human chromosome 16 cloneRP11-327F22, which includes NOD2, is set forth in, e.g., GenbankAccession No. AC007728. Furthermore, the sequence of NOD2 from otherspecies can be found in the GenBank database.

The NOD2 protein contains amino-terminal caspase recruitment domains(CARDs), which can activate NF-kappa B (NF-kB), and severalcarboxy-terminal leucine-rich repeat domains (Ogura et al., J. Biol.Chem., 276:4812-4818 (2001)). NOD2 has structural homology with theapoptosis regulator Apaf-1/CED-4 and a class of plant disease resistantgene products (Ogura et al., supra). Similar to plant disease resistantgene products, NOD2 has an amino-terminal effector domain, anucleotide-binding domain and leucine rich repeats (LRRs). Wild-typeNOD2 activates nuclear factor NF-kappa B, making it responsive tobacterial lipopolysaccharides (LPS; Ogura et al., supra; Inohara et al.,J. Biol. Chem., 276:2551-2554 (2001). NOD2 can function as anintercellular receptor for LPS, with the leucine rich repeats requiredfor responsiveness.

Variations at three single nucleotide polymorphisms in the coding regionof NOD2 have been previously described. These three SNPs, designatedR702W (“SNP 8”), G908R (“SNP 12”), and 1007fs (“SNP 13”), are located inthe carboxy-terminal region of the NOD2 gene (Hugot et al., supra). Afurther description of SNP 8, SNP 12, and SNP 13, as well as additionalSNPs in the NOD2 gene suitable for use in the invention, can be foundin, e.g., U.S. Pat. Nos. 6,835,815; 6,858,391; and 7,592,437; and U.S.Patent Publication Nos. 20030190639, 20050054021, and 20070072180.

In some embodiments, a NOD2 variant is located in a coding region of theNOD2 locus, for example, within a region encoding several leucine-richrepeats in the carboxy-terminal portion of the NOD2 polypeptide. SuchNOD2 variants located in the leucine-rich repeat region of NOD2 include,without limitation, R702W (“SNP 8”) and G908R (“SNP 12”). A NOD2 variantuseful in the invention can also encode a NOD2 polypeptide with reducedability to activate NF-kappa B as compared to NF-kappa B activation by awild-type NOD2 polypeptide. As a non-limiting example, the NOD2 variant1007fs (“SNP 13”) results in a truncated NOD2 polypeptide which hasreduced ability to induce NF-kappa B in response to LPS stimulation(Ogura et al., Nature, 411:603-606 (2001)).

A NOD2 variant useful in the invention can be, for example, R702W,G908R, or 1007fs. R702W, G908R, and 1007fs are located within the codingregion of NOD2. In one embodiment, a method of the invention ispracticed with the R702W NOD2 variant. As used herein, the term “R702W”includes a single nucleotide polymorphism within exon 4 of the NOD2gene, which occurs within a triplet encoding amino acid 702 of the NOD2protein. The wild-type NOD2 allele contains a cytosine (c) residue atposition 138,991 of the AC007728 sequence, which occurs within a tripletencoding an arginine at amino acid 702. The R702W NOD2 variant containsa thymine (t) residue at position 138,991 of the AC007728 sequence,resulting in an arginine (R) to tryptophan (W) substitution at aminoacid 702 of the NOD2 protein. Accordingly, this NOD2 variant is denoted“R702W” or “702W” and can also be denoted “R675W” based on the earliernumbering system of Hugot et al., supra. In addition, the R702W variantis also known as the “SNP 8” allele or a “2” allele at SNP 8. The NCBISNP ID number for R702W or SNP 8 is rs2066844. The presence of the R702WNOD2 variant and other NOD2 variants can be conveniently detected, forexample, by allelic discrimination assays or sequence analysis.

A method of the invention can also be practiced with the G908R NOD2variant. As used herein, the term “G908R” includes a single nucleotidepolymorphism within exon 8 of the NOD2 gene, which occurs within atriplet encoding amino acid 908 of the NOD2 protein. Amino acid 908 islocated within the leucine rich repeat region of the NOD2 gene. Thewild-type NOD2 allele contains a guanine (g) residue at position 128,377of the AC007728 sequence, which occurs within a triplet encoding glycineat amino acid 908. The G908R NOD2 variant contains a cytosine (c)residue at position 128,377 of the AC007728 sequence, resulting in aglycine (G) to arginine (R) substitution at amino acid 908 of the NOD2protein. Accordingly, this NOD2 variant is denoted “G908R” or “908R” andcan also be denoted “G881R” based on the earlier numbering system ofHugot et al., supra. In addition, the G908R variant is also known as the“SNP 12” allele or a “2” allele at SNP 12. The NCBI SNP ID number forG908R SNP 12 is rs2066845.

A method of the invention can also be practiced with the 1007fs NOD2variant. This variant is an insertion of a single nucleotide thatresults in a frame shift in the tenth leucine-rich repeat of the NOD2protein and is followed by a premature stop codon. The resultingtruncation of the NOD2 protein appears to prevent activation ofNF-kappaB in response to bacterial lipopolysaccharides (Ogura et al.,supra). As used herein, the term “1007fs” includes a single nucleotidepolymorphism within exon 11 of the NOD2 gene, which occurs in a tripletencoding amino acid 1007 of the NOD2 protein. The 1007fs variantcontains a cytosine which has been added at position 121,139 of theAC007728 sequence, resulting in a frame shift mutation at amino acid1007. Accordingly, this NOD2 variant is denoted “1007fs” and can also bedenoted “3020insC” or “980fs” based on the earlier numbering system ofHugot et al., supra. In addition, the 1007fs NOD2 variant is also knownas the “SNP 13” allele or a “2” allele at SNP 13. The NCBI SNP ID numberfor 1007fs or SNP 13 is rs2066847.

One skilled in the art recognizes that a particular NOD2 variant alleleor other polymorphic allele can be conveniently defined, for example, incomparison to a Centre d'Etude du Polymorphisme Humain (CEPH) referenceindividual such as the individual designated 1347-02 (Dib et al.,Nature, 380:152-154 (1996)), using commercially available reference DNAobtained, for example, from PE Biosystems (Foster City, Calif.). Inaddition, specific information on SNPs can be obtained from the dbSNP ofthe National Center for Biotechnology Information (NCBI).

A NOD2 variant can also be located in a non-coding region of the NOD2locus. Non-coding regions include, for example, intron sequences as wellas 5′ and 3′ untranslated sequences. A non-limiting example of a NOD2variant allele located in a non-coding region of the NOD2 gene is theJW1 variant, which is described in Sugimura et al., Am. J. Hum. Genet.,72:509-518 (2003) and U.S. Patent Publication No. 20070072180. Examplesof NOD2 variant alleles located in the 3′ untranslated region of theNOD2 gene include, without limitation, the JW15 and JW16 variantalleles, which are described in U.S. Patent Publication No. 20070072180.Examples of NOD2 variant alleles located in the 5′ untranslated region(e.g., promoter region) of the NOD2 gene include, without limitation,the JW17 and JW18 variant alleles, which are described in U.S. PatentPublication No. 20070072180.

As used herein, the term “JW1 variant allele” includes a geneticvariation at nucleotide 158 of intervening sequence 8 (intron 8) of theNOD2 gene. In relation to the AC007728 sequence, the JW1 variant alleleis located at position 128,143. The genetic variation at nucleotide 158of intron 8 can be, but is not limited to, a single nucleotidesubstitution, multiple nucleotide substitutions, or a deletion orinsertion of one or more nucleotides. The wild-type sequence of intron 8has a cytosine at position 158. As non-limiting examples, a JW1 variantallele can have a cytosine (c) to adenine (a), cytosine (c) to guanine(g), or cytosine (c) to thymine (t) substitution at nucleotide 158 ofintron 8. In one embodiment, the JW1 variant allele is a change from acytosine (c) to a thymine (t) at nucleotide 158 of NOD2 intron 8.

The term “JW15 variant allele” includes a genetic variation in the 3′untranslated region of NOD2 at nucleotide position 118,790 of theAC007728 sequence. The genetic variation at nucleotide 118,790 can be,but is not limited to, a single nucleotide substitution, multiplenucleotide substitutions, or a deletion or insertion of one or morenucleotides. The wild-type sequence has an adenine (a) at position118,790. As non-limiting examples, a JW15 variant allele can have anadenine (a) to cytosine (c), adenine (a) to guanine (g), or adenine (a)to thymine (t) substitution at nucleotide 118,790. In one embodiment,the JW15 variant allele is a change from an adenine (a) to a cytosine(c) at nucleotide 118,790.

As used herein, the term “JW16 variant allele” includes a geneticvariation in the 3′ untranslated region of NOD2 at nucleotide position118,031 of the AC007728 sequence. The genetic variation at nucleotide118,031 can be, but is not limited to, a single nucleotide substitution,multiple nucleotide substitutions, or a deletion or insertion of one ormore nucleotides. The wild-type sequence has a guanine (g) at position118,031. As non-limiting examples, a JW16 variant allele can have aguanine (g) to cytosine (c), guanine (g) to adenine (a), or guanine (g)to thymine (t) substitution at nucleotide 118,031. In one embodiment,the JW16 variant allele is a change from a guanine (g) to an adenine (a)at nucleotide 118,031.

The term “JW17 variant allele” includes a genetic variation in the 5′untranslated region of NOD2 at nucleotide position 154,688 of theAC007728 sequence. The genetic variation at nucleotide 154,688 can be,but is not limited to, a single nucleotide substitution, multiplenucleotide substitutions, or a deletion or insertion of one or morenucleotides. The wild-type sequence has a cytosine (c) at position154,688. As non-limiting examples, a JW17 variant allele can have acytosine (c) to guanine (g), cytosine (c) to adenine (a), or cytosine(c) to thymine (t) substitution at nucleotide 154,688. In oneembodiment, the JW17 variant allele is a change from a cytosine (c) to athymine (t) at nucleotide 154,688.

As used herein, the term “JW18 variant allele” includes a geneticvariation in the 5′ untranslated region of NOD2 at nucleotide position154,471 of the AC007728 sequence. The genetic variation at nucleotide154,471 can be, but is not limited to, a single nucleotide substitution,multiple nucleotide substitutions, or a deletion or insertion of one ormore nucleotides. The wild-type sequence has a cytosine (c) at position154,471. As non-limiting examples, a JW18 variant allele can have acytosine (c) to guanine (g), cytosine (c) to adenine (a), or cytosine(c) to thymine (t) substitution at nucleotide 154,471. In oneembodiment, the JW18 variant allele is a change from a cytosine (c) to athymine (t) at nucleotide 154,471.

It is understood that the methods of the invention can be practiced withthese or other NOD2 variant alleles located in a coding region ornon-coding region (e.g., intron or promoter region) of the NOD2 locus.It is further understood that the methods of the invention can involvedetermining the presence of one, two, three, four, or more NOD2variants, including, but not limited to, the SNP 8, SNP 12, and SNP 13alleles, and other coding as well as non-coding region variants.

II. STATISTICAL ANALYSIS

In some aspects, the present invention provides methods for selectinganti-TNF drug therapy, optimizing anti-TNF drug therapy, reducingtoxicity associated with anti-TNF drug therapy, and/or monitoring theefficacy of anti-TNF drug treatment by applying a statistical algorithmto one or more (e.g., a combination of two, three, four, five, six,seven, or more) biochemical markers, serological markers, and/or geneticmarkers to generate a disease activity profile (DAP). In particularembodiments, quantile analysis is applied to the presence, level, and/orgenotype of one or more markers to guide treatment decisions forpatients receiving anti-TNF drug therapy. In other embodiments, one or acombination of two of more learning statistical classifier systems areapplied to the presence, level, and/or genotype of one or more markersto guide treatment decisions for patients receiving anti-TNF drugtherapy. The statistical analyses of the methods of the presentinvention advantageously provide improved sensitivity, specificity,negative predictive value, positive predictive value, and/or overallaccuracy for selecting an initial anti-TNF drug therapy and fordetermining when or how to adjust or modify (e.g., increase or decrease)the subsequent dose of an anti-TNF drug, to combine an anti-TNF drug(e.g., at an increased, decreased, or same dose) with one or moreimmunosuppressive agents such as methotrexate (MTX) or azathioprine(AZA), and/or to change the current course of therapy (e.g., switch to adifferent anti-TNF drug).

The term “statistical analysis” or “statistical algorithm” or“statistical process” includes any of a variety of statistical methodsand models used to determine relationships between variables. In thepresent invention, the variables are the presence, level, or genotype ofat least one marker of interest. Any number of markers can be analyzedusing a statistical analysis described herein. For example, the presenceor level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more markers can beincluded in a statistical analysis. In one embodiment, logisticregression is used. In another embodiment, linear regression is used. Inyet another embodiment, ordinary least squares regression orunconditional logistic regression is used. In certain preferredembodiments, the statistical analyses of the present invention comprisea quantile measurement of one or more markers, e.g., within a givenpopulation, as a variable. Quantiles are a set of “cut points” thatdivide a sample of data into groups containing (as far as possible)equal numbers of observations. For example, quartiles are values thatdivide a sample of data into four groups containing (as far as possible)equal numbers of observations. The lower quartile is the data value aquarter way up through the ordered data set; the upper quartile is thedata value a quarter way down through the ordered data set. Quintilesare values that divide a sample of data into five groups containing (asfar as possible) equal numbers of observations. The present inventioncan also include the use of percentile ranges of marker levels (e.g.,tertiles, quartile, quintiles, etc.), or their cumulative indices (e.g.,quartile sums of marker levels to obtain quartile sum scores (QSS),etc.) as variables in the statistical analyses (just as with continuousvariables).

In certain embodiments, the present invention involves detecting ordetermining the presence, level (e.g., magnitude), and/or genotype ofone or more markers of interest using quartile analysis. In this type ofstatistical analysis, the level of a marker of interest is defined asbeing in the first quartile (<25%), second quartile (25-50%), thirdquartile (51%-<75%), or fourth quartile (75-100%) in relation to areference database of samples. These quartiles may be assigned aquartile score of 1, 2, 3, and 4, respectively. In certain instances, amarker that is not detected in a sample is assigned a quartile score of0 or 1, while a marker that is detected (e.g., present) in a sample(e.g., sample is positive for the marker) is assigned a quartile scoreof 4. In some embodiments, quartile 1 represents samples with the lowestmarker levels, while quartile 4 represent samples with the highestmarker levels. In other embodiments, quartile 1 represents samples witha particular marker genotype (e.g., wild-type allele), while quartile 4represent samples with another particular marker genotype (e.g., allelicvariant). The reference database of samples can include a large spectrumof patients with a TNFα-mediated disease or disorder such as, e.g., IBD.From such a database, quartile cut-offs can be established. Anon-limiting example of quartile analysis suitable for use in thepresent invention is described in, e.g., Mow et al., Gastroenterology,126:414-24 (2004).

In some embodiments, the statistical analyses of the present inventioncomprise one or more learning statistical classifier systems. As usedherein, the term “learning statistical classifier system” includes amachine learning algorithmic technique capable of adapting to complexdata sets (e.g., panel of markers of interest) and making decisionsbased upon such data sets. In some embodiments, a single learningstatistical classifier system such as a decision/classification tree(e.g., random forest (RF) or classification and regression tree (C&RT))is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9,10, or more learning statistical classifier systems are used, preferablyin tandem. Examples of learning statistical classifier systems include,but are not limited to, those using inductive learning (e.g.,decision/classification trees such as random forests, classification andregression trees (C&RT), boosted trees, etc.), Probably ApproximatelyCorrect (PAC) learning, connectionist learning (e.g., neural networks(NN), artificial neural networks (ANN), neuro fuzzy networks (NFN),network structures, the Cox Proportional-Hazards Model (CPHM),perceptrons such as multi-layer perceptrons, multi-layer feed-forwardnetworks, applications of neural networks, Bayesian learning in beliefnetworks, etc.), reinforcement learning (e.g., passive learning in aknown environment such as naïve learning, adaptive dynamic learning, andtemporal difference learning, passive learning in an unknownenvironment, active learning in an unknown environment, learningaction-value functions, applications of reinforcement learning, etc.),and genetic algorithms and evolutionary programming. Other learningstatistical classifier systems include support vector machines (e.g.,Kernel methods), multivariate adaptive regression splines (MARS),Levenberg-Marquardt algorithms, Gauss-Newton algorithms, mixtures ofGaussians, gradient descent algorithms, and learning vector quantization(LVQ).

Random forests are learning statistical classifier systems that areconstructed using an algorithm developed by Leo Breiman and AdeleCutler. Random forests use a large number of individual decision treesand decide the class by choosing the mode (i.e., most frequentlyoccurring) of the classes as determined by the individual trees. Randomforest analysis can be performed, e.g., using the RandomForests softwareavailable from Salford Systems (San Diego, Calif.). See, e.g., Breiman,Machine Learning, 45:5-32 (2001); andhttp://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,for a description of random forests.

Classification and regression trees represent a computer intensivealternative to fitting classical regression models and are typicallyused to determine the best possible model for a categorical orcontinuous response of interest based upon one or more predictors.Classification and regression tree analysis can be performed, e.g.,using the C&RT software available from Salford Systems or the Statisticadata analysis software available from StatSoft, Inc. (Tulsa, Okla.). Adescription of classification and regression trees is found, e.g., inBreiman et al. “Classification and Regression Trees,” Chapman and Hall,New York (1984); and Steinberg et al., “CART: Tree-StructuredNon-Parametric Data Analysis,” Salford Systems, San Diego, (1995).

Neural networks are interconnected groups of artificial neurons that usea mathematical or computational model for information processing basedon a connectionist approach to computation. Typically, neural networksare adaptive systems that change their structure based on external orinternal information that flows through the network. Specific examplesof neural networks include feed-forward neural networks such asperceptrons, single-layer perceptrons, multi-layer perceptrons,backpropagation networks, ADALINE networks, MADALINE networks,Learnmatrix networks, radial basis function (RBF) networks, andself-organizing maps or Kohonen self-organizing networks; recurrentneural networks such as simple recurrent networks and Hopfield networks;stochastic neural networks such as Boltzmann machines; modular neuralnetworks such as committee of machines and associative neural networks;and other types of networks such as instantaneously trained neuralnetworks, spiking neural networks, dynamic neural networks, andcascading neural networks. Neural network analysis can be performed,e.g., using the Statistica data analysis software available fromStatSoft, Inc. See, e.g., Freeman et al., In “Neural Networks:Algorithms, Applications and Programming Techniques,” Addison-WesleyPublishing Company (1991); Zadeh, Information and Control, 8:338-353(1965); Zadeh, “IEEE Trans. on Systems, Man and Cybernetics,” 3:28-44(1973); Gersho et al., In “Vector Quantization and Signal Compression,”Kluywer Academic Publishers, Boston, Dordrecht, London (1992); andHassoun, “Fundamentals of Artificial Neural Networks,” MIT Press,Cambridge, Mass., London (1995), for a description of neural networks.

Support vector machines are a set of related supervised learningtechniques used for classification and regression and are described,e.g., in Cristianini et al., “An Introduction to Support Vector Machinesand Other Kernel-Based Learning Methods,” Cambridge University Press(2000). Support vector machine analysis can be performed, e.g., usingthe SVM^(light) software developed by Thorsten Joachims (CornellUniversity) or using the LIBSVM software developed by Chih-Chung Changand Chih-Jen Lin (National Taiwan University).

The various statistical methods and models described herein can betrained and tested using a cohort of samples (e.g., serological and/orgenomic samples) from healthy individuals and patients with aTNFα-mediated disease or disorder such as, e.g., IBD (e.g., CD and/orUC). For example, samples from patients diagnosed by a physician,preferably by a gastroenterologist, as having IBD or a clinical subtypethereof using a biopsy, colonoscopy, or an immunoassay as described in,e.g., U.S. Pat. No. 6,218,129, are suitable for use in training andtesting the statistical methods and models of the present invention.Samples from patients diagnosed with IBD can also be stratified intoCrohn's disease or ulcerative colitis using an immunoassay as describedin, e.g., U.S. Pat. Nos. 5,750,355 and 5,830,675. Samples from healthyindividuals can include those that were not identified as IBD samples.One skilled in the art will know of additional techniques and diagnosticcriteria for obtaining a cohort of patient samples that can be used intraining and testing the statistical methods and models of the presentinvention.

As used herein, the term “sensitivity” includes the probability that amethod of the present invention for selecting anti-TNF drug therapy,optimizing anti-TNF drug therapy, reducing toxicity associated withanti-TNF drug therapy, and/or monitoring the efficacy of anti-TNF drugtreatment gives a positive result when the sample is positive, e.g.,having the predicted therapeutic response to anti-TNF drug therapy ortoxicity associated with anti-TNF drug therapy. Sensitivity iscalculated as the number of true positive results divided by the sum ofthe true positives and false negatives. Sensitivity essentially is ameasure of how well the present invention correctly identifies those whohave the predicted therapeutic response to anti-TNF drug therapy ortoxicity associated with anti-TNF drug therapy from those who do nothave the predicted therapeutic response or toxicity. The statisticalmethods and models can be selected such that the sensitivity is at leastabout 60%, and can be, e.g., at least about 65%, 70%, 75%, 76%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The term “specificity” includes the probability that a method of thepresent invention for selecting anti-TNF drug therapy, optimizinganti-TNF drug therapy, reducing toxicity associated with anti-TNF drugtherapy, and/or monitoring the efficacy of anti-TNF drug treatment givesa negative result when the sample is not positive, e.g., not having thepredicted therapeutic response to anti-TNF drug therapy or toxicityassociated with anti-TNF drug therapy. Specificity is calculated as thenumber of true negative results divided by the sum of the true negativesand false positives. Specificity essentially is a measure of how wellthe present invention excludes those who do not have the predictedtherapeutic response to anti-TNF drug therapy or toxicity associatedwith anti-TNF drug therapy from those who do have the predictedtherapeutic response or toxicity. The statistical methods and models canbe selected such that the specificity is at least about 60%, and can be,e.g., at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%.

The term “negative predictive value” or “NPV” includes the probabilitythat an individual identified as not having the predicted therapeuticresponse to anti-TNF drug therapy or toxicity associated with anti-TNFdrug therapy actually does not have the predicted therapeutic responseor toxicity. Negative predictive value can be calculated as the numberof true negatives divided by the sum of the true negatives and falsenegatives. Negative predictive value is determined by thecharacteristics of the methods of the present invention as well as theprevalence of the disease in the population analyzed. The statisticalmethods and models can be selected such that the negative predictivevalue in a population having a disease prevalence is in the range ofabout 70% to about 99% and can be, for example, at least about 70%, 75%,76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The term “positive predictive value” or “PPV” includes the probabilitythat an individual identified as having the predicted therapeuticresponse to anti-TNF drug therapy or toxicity associated with anti-TNFdrug therapy actually has the predicted therapeutic response ortoxicity. Positive predictive value can be calculated as the number oftrue positives divided by the sum of the true positives and falsepositives. Positive predictive value is determined by thecharacteristics of the methods of the present invention as well as theprevalence of the disease in the population analyzed. The statisticalmethods and models can be selected such that the positive predictivevalue in a population having a disease prevalence is in the range ofabout 70% to about 99% and can be, for example, at least about 70%, 75%,76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

Predictive values, including negative and positive predictive values,are influenced by the prevalence of the disease in the populationanalyzed. In the present invention, the statistical methods and modelscan be selected to produce a desired clinical parameter for a clinicalpopulation with a particular prevalence for a TNFα-mediated disease ordisorder such as, e.g., IBD. As a non-limiting example, statisticalmethods and models can be selected for an IBD prevalence of up to about1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%,45%, 50%, 55%, 60%, 65%, or 70%, which can be seen, e.g., in aclinician's office such as a gastroenterologist's office or a generalpractitioner's office.

As used herein, the term “overall agreement” or “overall accuracy”includes the accuracy with which a method of the present inventionselects anti-TNF drug therapy, optimizes anti-TNF drug therapy, reducestoxicity associated with anti-TNF drug therapy, and/or monitors theefficacy of anti-TNF drug treatment. Overall accuracy is calculated asthe sum of the true positives and true negatives divided by the totalnumber of sample results and is affected by the prevalence of thedisease in the population analyzed. For example, the statistical methodsand models can be selected such that the overall accuracy in a patientpopulation having a disease prevalence is at least about 40%, and canbe, e.g., at least about 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%,49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%,63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%,77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

III. EXAMPLES

The present invention will be described in greater detail by way ofspecific examples. The following examples are offered for illustrativepurposes, and are not intended to limit the invention in any manner.Those of skill in the art will readily recognize a variety ofnoncritical parameters which can be changed or modified to yieldessentially the same results.

The Examples set forth in U.S. Provisional Application No. 61/444,097,filed Feb. 17, 2011, and PCT Application No. PCT/US2010/054125, filedOct. 26, 2010, are hereby incorporated by reference in their entiretyfor all purposes.

Example 1. Disease Activity Profiling for Identifying Responders andNon-Responders to Anti-TNFα Biologics

This example describes methods for personalized therapeutic managementof a TNFα-mediated disease in order to optimize therapy or monitortherapeutic efficacy in a subject using the disease activity profilingof the present invention to identify subjects as responders ornon-responders to anti-TNF drug therapy.

FIG. 1 illustrates an exemplary IBD wound response profile in whichwound progression is divided into inflammatory, proliferative, andremodeling phases. As non-limiting examples, inflammatory response phasemarkers tested include: anti-TNF drugs such as Remicade (infliximab);anti-drug antibodies (ADA) such as HACA; inflammatory markers such asGM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, and sTNF RII; andanti-inflammatory markers such as IL-12p70 and IL-10. Non-limitingexamples of proliferation response phase markers tested include tissuerepair/remodeling factors (also referred to as mucosal healing markers)such as AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF,TGF-α, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, andTWEAK.

A COMMIT (Combination Of Maintenance Methotrexate-Infliximab Trial)study was performed to evaluate the safety and efficacy of Remicade(infliximab) in combination with methotrexate for the long-termtreatment of Crohn's disease (CD). Treatment success was defined by theproportion of subjects in clinical remission (i.e., completediscontinuation of prednisone therapy and a Crohn's Disease ActivityIndex (CDAI) score of <150) at week 14, and maintenance of clinicalremission between study weeks 14 and 50. In particular, clinicalassessment with CDAI was performed at week 0, 46, 50, and 66. Subjectswith CDAI>150 were identified as non-responders. Additional informationon the COMMIT study is provided at clinicaltrials.gov, the disclosure ofwhich is incorporated by reference in its entirety for all purposes.

Disease activity profiling was performed on a number of subjects in theCOMMIT study. In particular, the following array of markers weremeasured at various time points during treatment with Remicade(infliximab) only or a combination of Remicade (infliximab) withmethotrexate: (1) Remicade (infliximab) and HACA; (2) inflammatorymarkers GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, and sTNF RII; (3)anti-inflammatory markers IL-12p70 and IL-10; and (4) tissue repairmarkers EGF, bFGF, PIGF, sFlt1, and VEGF. The disease activity profile(DAP) for 7 of these subjects, which provides a comparison betweenresponder and non-responder profiles, is illustrated herein. Thesepatient examples show that markers for inflammation and tissue repaircorrelated with infliximab and HACA levels in select active CD patients,certain markers may predict the disease activity profile, and diseaseactivity profiling will further guide patient therapy and identifymucosal healing markers. In addition, these patient examples show thatwhenever anti-inflammatory cytokines such as IL-12p70 and IL-10 areelevated, the patient responds, indicating that they may be markers ofmucosal healing, and that tissue repair markers (TRM) go up innon-responders.

Table of Personalized Disease Activity Profiling: Levels of IFX, HACA,Inflammatory Markers, Anti-Inflammatory Markers, and Mucosal HealingMarkers Anti- Mucosal Patient Treatment Clinical Inflammatoryinflammatory Healing ID No. Regimen CDAI Definition IFX HACA MarkersMarkers Markers 12209 IFX + MTX t = 0, CDAI was Non- Low at HACA+, HIGHLOW MEDIUM 202. responder trough LOW t = wk 26, CDAI (wk 14) was 183 t =wk 66, CDAI = 152. 11010 IFX t = 0, CDAI was Responder High at HACA−,LOW HIGH HIGH 262. trough ND t = wk 46, CDAI (wk 14) was 85. 10118 IFX t= 0, CDAI was Responder High at HACA−, MEDIUM HIGH HIGH 251. trough ND t= wk 46, CDAI (wk 14) was 109. 11602 IFX + MTX t = 0, CDAI was ResponderHigh at HACA−, LOW HIGH HIGH 217. trough ND t = wk 46, CDAI (wk 14) was68. 11505 IFX t = 0, CDAI was Non- Very Low HACA++, MEDIUM LOW HIGH 272.responder at trough HIGH t = wk 46, CDAI (wk 14) was 145. t = wk 66,CDAI = 195. 11601 IFX + MTX t = 0, CDAI was Responder High at HACA+,HIGH HIGH MEDIUM 207. trough LOW t = wk 46, CDAI (wk 14) was 0. IFX =infliximab. MTX = methotrexate. ND = no detectable level of HACA.Patient 12209: Infliximab+Methotrexate (MTX) Treated.

CDAI at time 0 was 202. At week 46, CDAI was 183 (“Delta 19” or202−19=183). At week 66, CDAI was 152 (“Delta 50” or 202−50=152).Clinically defined as non-responder. Disease activity profile (DAP)accurately identified this patient. In particular, DAP showed that thispatient had low infliximab (IFX) levels at trough (“T”; Week 14), thepresence of a detectable concentration level of HACA (“HACA+”), highinflammatory marker levels, low anti-inflammatory marker levels, andmedium tissue repair marker (TRM) levels. Suggested alternativetreatment options may include, for example, increasing the dose of IFX,switching to therapy with adalimumab (HUMIRA™), treating with adifferent immunosuppressive drug such as azathioprine (AZA), and/orswitching to therapy with a drug that targets a different mechanism(e.g., an anti-INFγ antibody such as fontolizumab).

Patient 11010: Infliximab Treated.

CDAI at week 0 was 262. At week 46, CDAI was 85 (“Delta 177” or262−177=85). Clinical responder. Disease activity profile (DAP)accurately identified this patient. In particular, DAP showed that thispatient had high infliximab (IFX) levels at trough (“T”; Week 14), nodetectable level of HACA (“HACA−−”), low inflammatory marker levels,high anti-inflammatory marker levels, and high tissue repair marker(TRM) levels. For example, anti-inflammatory cytokines IL-12p70 andIL-10 were high. As shown with the patients in this example, wheneveranti-inflammatory cytokines were high, the patient responded mostprobably with mucosal healing. In addition, bFGF concentration levelswere low at all time points, although other TRM levels were high,indicating that tissue growth was muted, such that tissue repair hadalready occurred.

Patient 10118: Infliximab Treated.

CDAI at week 0 was 251. At week 46, CDAI was 109 (“Delta 142” or251−142=109). Clinical responder. Disease activity profile (DAP)accurately identified this patient. In particular, DAP showed that thispatient had high infliximab (IFX) levels at trough (“T”; Week 14), nodetectable level of HACA (“HACA−−”), medium inflammatory marker levels,high anti-inflammatory marker levels, and high tissue repair marker(TRIM) levels. For example, anti-inflammatory cytokines IL-12p70 andIL-10 were high. Again, as shown with the patients in this example,whenever anti-inflammatory cytokines were high, the patient respondedmost probably with mucosal healing. In addition, bFGF concentrationlevels were low at all time points and remained flat over the course oftherapy, although other TRM levels were higher, indicating that tissuegrowth was muted, such that tissue repair had already occurred.

Patient 11602: Infliximab+Methotrexate (MTX) Treated.

CDAI at week 0 was 217. At week 46, CDAI was 68 (“Delta 149” or217−149=68). Clinical responder. Disease activity profile (DAP)accurately identified this patient. In particular, DAP showed that thispatient had high infliximab (IFX) levels at trough (“T”; Week 14), nodetectable level of HACA (“HACA−−”), low inflammatory marker levels,high anti-inflammatory marker levels, and high tissue repair marker(TRM) levels. For example, anti-inflammatory cytokines IL-12p70 andIL-10 were high. Again, as shown with the patients in this example,whenever anti-inflammatory cytokines were high, the patient respondedmost probably with mucosal healing. In addition, bFGF concentrationlevels were lower at all time points compared to the other TRM levels,indicating that tissue growth was muted, such that tissue repair hadalready occurred.

Patient 11505: Infliximab Treated.

CDAI at time 0 was 272. At week 46, CDAI was 145 (“Delta 127” or272−127=145). At week 66, CDAI was 195. Clinically defined asnon-responder. Disease activity profile (DAP) accurately identified thispatient. In particular, DAP showed that this patient had very lowinfliximab (IFX) levels at trough (“T”; Week 14), a high concentrationlevel of HACA (“HACA++”), medium inflammatory marker levels, lowanti-inflammatory marker levels, and high tissue repair marker (TRM)levels. In non-responders, the levels of TRM such as bFGF go up, whilein responders they either go down or do not change. Suggestedalternative treatment options may include, for example, increasing thedose of IFX, switching to therapy with adalimumab (HUMIRA™), treatingwith an immunosuppressive drug such as MTX or azathioprine (AZA), and/orswitching to therapy with a drug that targets a different mechanism(e.g., an anti-INFγ antibody such as fontolizumab).

Patient 11601: Infliximab+Methotrexate (MTX) Treated.

CDAI at week 0 was 207. At week 46, CDAI was 0 (“Delta 207” or207−207=0). The patient was clinically defined as responder. Diseaseactivity profile (DAP) accurately identified this patient. Inparticular, DAP showed that this patient had high infliximab (IFX)levels at trough (“T”; Week 14), low HACA levels (“HACA+”), highinflammatory marker levels, high anti-inflammatory marker levels, andmedium tissue repair marker (TRM) levels. For example, anti-inflammatorycytokines IL-12p70 and IL-10 were high. Again, as shown with thepatients in this example, whenever anti-inflammatory cytokines werehigh, the patient responded most probably with mucosal healing, clearlyindicating that anti-inflammatory markers are very important. Thepresence of high inflammation may be due to complication.

Patient 10113: Infliximab Treated.

CDAI at time 0 was 150. At week 46, CDAI was 96 (“Delta 54” or150−54=96). At visit 10 (“V10”), CDAI was 154, and at visit 11 (“V11”),CDAI was 169. As such, CDAI started at 150 and stayed around 150. Thepatient was clinically defined as non-responder. Disease activityprofile (DAP) accurately identified this patient. In particular, DAPshowed that this patient had low infliximab (IFX) levels at trough (“T”;Week 14), a detectable concentration level of HACA (“HACA+”), mediuminflammatory marker levels, low anti-inflammatory marker levels, andmedium tissue repair marker (TRM) levels. Again, TRM levels go up innon-responders, while in responders they either go down or do notchange. Suggested alternative treatment options may include, forexample, increasing the dose of IFX, switching to therapy withadalimumab (HUMIRA™), treating with an immunosuppressive drug such asMTX or azathioprine, and/or switching to therapy with a drug thattargets a different mechanism (e.g., an anti-INFγ antibody such asfontolizumab).

Example 2. Disease Activity Profiling Modeling

An exemplary 3-dimensional graph rendering of the disease activityprofile (DAP) of the present invention includes each of the differentmarkers present in the array of markers on the x-axis, normalized markerlevels on the y-axis, and time on the z-axis (e.g., time points whereinsamples are taken and marker levels measured). An exemplary topographicmap of the DAP of the present invention (also referred to herein as apersonalized disease profile) includes each of the different markerspresent in the array of markers the y-axis, time on the x-axis (e.g.,time points wherein samples are taken and marker levels measured), andrelative marker levels in grayscale.

The 3D models described herein represent a novel paradigm for treatmentbecause they are individualized and titratable such that doseadjustments are made in a personalized manner. For example, markerpanels including markers such as inflammatory, proliferative, andremodeling markers enable a determination in real-time of the bestcourse of treatment for a patient on therapy such as anti-TNF drugtherapy, e.g., for treating CD or RA. As a result, both the time courseand the concentration levels of markers in the panel or array of markersare important for therapy adjustment and monitoring to personalize andindividualize therapy and determine optimal doses or dose adjustments.In certain instances, the change in one or more marker levels over timeis an important consideration for therapy adjustment and monitoring. Inparticular embodiments, the desired therapeutic zone for the set or asubset of the markers in the array or panel is within a defined range inthe 3D graph or topographic map.

Example 3. Infliximab Non-Detection

This example represents a model for “time-to-event.” In other words,this example uses the Cox Proportional-Hazards Model (CPHM) to model thetime it takes for “an event” to occur and the risk of such an eventhappening. The model is a regression analysis with “time-to-event” onthe Y axis, which is a response variable, and “predictor variables” onthe X axis. In this example, the non-detection of infliximab (i.e., theconcentration of infliximab falling below a detection threshold) is theevent, with the potential predictors of such an event being biomarkers:e.g., CRP, IL-2, VEGF, and the like and or clinical information such asage, MTX treatment, gender, and the like.

In this example, the “Hazard” is the risk of infliximab not beingdetected (e.g., non-detection) by an analytical assay such as a mobilityshift assay. For example, FIG. 11 shows infliximab concentration levelsfor various patients during their course of treatment. An event occursin this example when the concentration of infliximab falls below apredetermined detection threshold. In certain instances, the CPHM isbeing used to predict the risk of the event occurring (infliximabnon-detection). The example also identifies biomarkers indicative ofsuch a risk occurring.

Using the CPHM, time is modeled until infliximab is not detectable by amobility shift assay. In the model, the predetermined threshold is 0.67μg/mL, which is the lower bound of the reference range. If theinfliximab concentration level is less than the threshold at time “t,”then the event has occurred at time “t.” In FIG. 12, patients wereranked by their time to the event. The event occurred for variouspatients at different points during treatment and is denoted with abullet point.

In the initial model, there were various markers and clinicalinformation used to predict the hazard or the risk of infliximabnon-detection by the mobility shift assay. These markers included thefollowing markers in the Table:

EGF IL-1β VCAM-1 bFGF IL-2 AGE PIGF IL-6 Months since diagnosis sFlt1IL-8 Disease @ colon VEGF TNF-α Disease @ small intestine GM-CSF sTNFRIIMTX treatment IFN-γ CRP Success IL-10 SAA IL-12p70 ICAM-1

From the initial marker list, the following list was derived as beingthe preferred markers indicative of the event:

GM-CSF sRNFRII Disease @ small intestine IL-2 SAA Success IL-6 ICAM-1TNF-α Months since Dx

The following Table lists the significant predictors of infliximabnon-detection risk or the hazard:

Predictor coef exp(coef) se(coef) p GM-CSF −1.92E−01 0.826 9.48E−024.34E−02 IL-2 1.42E−01 1.153 1.92E−02 1.63E−13 TNF-α 2.33E−02 1.0247.57E−03 2.11E−03 sTNFRII 3.57E−01 1.429 5.76E−02 5.67E−10 SAA 6.13E−061.000 1.90E−06 1.25E−03 Months since −3.20E−03 0.997 1.45E−03 2.68E−02Dx Disease @ 1.10E+00 2.995 4.46E−01 1.39E−02 small intestine Success8.84E−01 2.421 3.13E−01 4.72E−03

The results in the above Table indicate the following are predictors ofthe hazard i.e., risk of the non-detection of infliximab:

GM-CSF: holding all other variables constant, an extra ng/μl of GM-CSFreduces the weekly hazard of infliximab non-detection by a factor of0.826, or 17.4%.

IL-2: An additional 1 ng/μl of IL-2 increases the hazard by a factor of1.153, or 15.3%.

TNF-α A 1 ng/μl of TNF-α increases the hazard by a factor of 1.024/2.4%.

sTNFRII: A 1 ng/μl of sTNFRII increases the hazard by a factor of1.429/42.9%.

SAA: A 1 ng/μl of SAA increases the hazard by a factor of1.000006/0.0006%, which is very small, but still a detectable effect(small SE).

Months since diagnosis: Each additional month since diagnosis decreasesthe hazard by a factor of 0.997, or 0.3%.

Disease site at the small intestine (categorical variable): If thedisease is located at the small intestine, the hazard is increased by afactor of 2.995, or nearly 200%.

Success (categorical variable): Also a predictive of hazard; innon-successful patients the hazard is increased by a factor of 2.421 or142%.

In summary, the following markers appear to be good predictors ofinfliximab “clearance”/or non-detection: 1) GM-CSF; 2) IL-2; 3) TNF-α;4) sTNFRII; and 5) the disease being situated in the small intestine.

As such, in one embodiment, the present invention provides: A method forpredicting the likelihood the concentration of an anti-TNF therapeuticor antibody during the course of treatment will fall below a thresholdvalue, the method comprising:

measuring a panel of markers selected from the group consisting of 1)GM-CSF; 2) IL-2; 3) TNF-α; 4) sTNFRII; and 5) the disease being situatedin the small intestine; and

predicting the likelihood the concentration of an anti-TNF therapeuticor antibody will fall below the threshold based upon the concentrationof the markers.

Example 4. Detection of Antidrug Antibody to Infliximab (“ATI” or“HACA”)

This example uses the Cox Proportional-Hazards Model (CPHM) to model thetime that it takes for an event to occur. This is a similar analysis toExample 3 above, but with the appearance of the anti-drug antibody alsoknown as ATI or HACA as the event and risk of ATI formation (detection)as the hazard. FIG. 13 shows the concentration of ATI (HACA) in variouspatients during the course of treatment. In FIG. 14, patients wereranked by their time to the event. The event occurred for variouspatients at different points during treatment and is denoted with abullet point. The risk of ATI detection is the hazard. Significantpredictors of the hazard include:

Predictor coef exp(coef) se(coef) p EGF −2.33E−03 0.998 1.18E−037.82E−03 VEGF 1.37E−03 1.001 4.10E−04 8.64E−04 GM-CSF −2.72E−01 0.7621.06E−01 1.06E−02 IL-2 6.15E−01 1.850 2.81E−01 2.83E−02 IL-8 3.58E−041.000 1.22E−04 3.25E−03 TNF-α 2.37E−02 1.024 8.76E−03 6.81E−03 CRP3.09E−05 1.000 1.04E−05 3.00E−03 VCAM 1.28E−03 1.001 2.01E−04 1.87E−10

The data in the above table indicates that EGF, VEGF, IL-8, CRP andVCAM-1 all have very small, but significant effects on the hazard.

GM-CSF: Holding all other variables constant, an extra ng/μl of GM-CSFreduces the weekly hazard of ATI detection by a factor of 0.762, or27.4%.

IL-2: A 1 ng/μl increase of IL-2 increases the hazard by a factor of1.85, or 85%.

TNF-α: A 1 ng/μl increase of TNF-α increases hazard by a factor of1.024, or 2.4%.

In summary, the Predictors of ATI detection hazard are GM-CSF, IL-2 andTNF-α.

As such, in one embodiment, the present invention provides a method forpredicting the likelihood that anti-drug antibodies will occur in anindividual on anti-TNF therapy or antibodies, said method comprising:

measuring a panel of markers selected from the group consisting of EGF,VEGF, IL-8, CRP and VCAM-1; and

predicting the likelihood that anti-drug antibodies will occur in anindividual on anti-TNF therapy based on the concentration of markerlevels.

Example 5. Disease Activity Profiling for Crohn's Disease PrognosisUsing COMMIT Study Samples

This example illustrates methods for personalized therapeutic managementof a TNFα-mediated disease in order to optimize therapy or monitortherapeutic efficacy in a subject using the disease activity profilingof the present invention. This examples illustrates disease activityprofiling which comprises detecting, measuring, or determining thepresence, level and or activation of one or more specific biomarkers(e.g., drug levels, anti-drug antibody levels, inflammatory markers,anti-inflammatory markers, and tissue repair markers).

This example describes disease activity profiling on a number of samplesfrom the COMMIT study. As described in Example 1, the COMMIT(Combination of Maintenance Methotrexate-Inflixamab Trial) study wasperformed to evaluate the safety and efficacy of Remicade (inflixamab)in combination with methotrexate (MTX) for the long-term treatment ofCrohn's Disease (CD). In particular, the following array of markers wasmeasured at various time points during treatment with Remicade(infliximab; IFX) only or a treatment of Remicade with MTX: (1) Remicade(inflixamab) and antidrug antibodies to infliximab (ATI); (2)inflammatory markers CRP, SAA, ICAM, VCAM; and (3) tissue repair markerVEGF. This example shows that the markers of inflammation and tissuerepair correlated with IFX and ATI levels in select patients of TNF-αmediated disease (e.g., Crohn's Disease and Ulcerative Colitis). In someinstances, arrays of markers may predict a disease activity index (e.g.,Crohn's Disease Activity Index). Analysis of the COMMIT study isillustrated herein.

The relationship between the presence of ATI and serum levels of IFXconcentration was investigated. For the evaluation, total ATI levelsbelow the level of quantitation (BLOQ) were 3.13 U/ml, and were set to0. IFX concentrations below the level of detection (BLOD) were set to 0.Per the sample comparison, only trough samples were used and a total of219 were used in the evaluation. 24 samples were determined to be ATIpositive (ATI+). It was determined that the median level of IFX was 0μg/ml in ATI+ samples, while the median level of IFX was 8.373 μg/ml inATI negative (ATI−) samples (p=3.71×10⁻⁹ by Mann Whitney U test). FIG.4A illustrates an association between the presence of ATI and the levelof IFX in patient samples. Patient samples with no detectable level ofATI had a significantly higher IFX median concentration, compared toATI+ samples.

The relationship between CDAI and the presence of ATI was evaluated. Inthe analysis ATI of 3.13 U/ml was set as the cut-off; only troughsamples were evaluated and ATI BLOQ was set as 0.195 samples were ATI−,while 24 samples from a total of 4 patients were ATI+. The resultsshowed that the median CDAI for ATI+ samples was 121.5 while the medianCDAI for ATI− samples was 82 (p=0.0132 by Mann Whitney U test). FIG. 4Billustrates that the presence of ATI correlates with higher CDAI. Theresults show that ATI+ samples have significantly higher CDAI than ATI−samples.

The relationship between the presence of ATI and combination therapy ofIFX and immunosuppressant agent (e.g., MTX) was investigated. ATI+samples at any trough time point were analyzed. The results showed thatthere was no significant difference in odds of having ATI between IFXtherapy alone and IFX+MTX combination therapy. The high odds ratio(e.g., 2.851) indicates that MTX can prevent a patient from developingan immune response to therapeutic biologics. FIG. 4C shows thatconcurrent immunosuppressant therapy (e.g., MTX) is more likely tosuppress the presence of ATI.

The relationship between ATI and clinical outcome at follow-up was alsoinvestigated. ATI+ samples at any trough time point were analyzed.Clinical outcome as described from the clinical data received from thestudy was parsed as either “success” or “non-success”. No significantdifference in odds of being ATI+ was seen regardless of treatmentregimen. The low odds ratio (e.g., 0.1855, p=0.1459) indicates that ATI+patients tend to have poor clinical outcomes. FIG. 5A shows thatpatients with ATI are more likely to develop a poor response totreatment.

This example also illustrates an association of an exemplary PROInflammatory Index and serum levels of infliximab (IFX) or the presenceof antibodies to IFX (ATI) in a patient sample. FIG. 5B illustrates thatthe inflammatory marker CRP is associated with increased levels of ATI.The data shows that the median CRP level was 8.11 μg/ml in ATI+ samplesand 1.73 μg/ml in ATI− samples (p=2.67×10⁻⁶ by Mann Whitney U Test).Other inflammatory and tissue repair markers were evaluated. FIG. 6illustrates that the protein levels of an array of one or moreinflammatory and tissue repair markers correlate to the formation ofantibodies to IFX. The data shows that of a combination of five markers(e.g., CRP, SAA, ICAM, VCAM, VEGF and including at least oneinflammatory marker) was expressed in 23 out of 24 ATI positive samples(FIG. 7A, grey box). The inflammatory marker SAA was found to bepositive in 19 of the 24 ATI positive samples that were also clinicallydescribed as having “high inflammation”. The results also show that VEGFand CRP are the most non-overlapping markers in the analysis.

This example further shows an exemplary PRO Inflammatory Index (PII).The inflammatory index score is created by logarithmic transformation ofa combination of values representing determined expression levels of aplurality of markers (e.g., PII=log(CRP+SAA+ICAM+VCAM+VEGF)). FIG. 7Billustrates that an exemplary PRO Inflammatory Index (PII) correlateswith levels of IFX (p<0.0001 and R²=−0.129) in patient samples of theCOMMIT study. The results show that ATI positive samples have asignificantly higher inflammatory index score compared to ATI negativesamples (P=6.4×10⁻⁸; see FIG. 7C).

As such, in one embodiment, the present invention provides a method formonitoring an infliximab treatment regimen, said method comprising:

-   -   a) measuring infliximab and antidrug antibodies to infliximab        (ATI);    -   b) measuring inflammatory markers CRP, SAA, ICAM, VCAM;    -   c) measuring tissue repair marker VEGF; and    -   d) correlating the measurements to therapeutic efficacy.

Example 6. Disease Activity Profiling for TNF-α Mediated DiseasePrognosis Using Clinical Study #1 Samples

This example describes methods for monitoring therapeutic efficacy in asubject using the disease activity profiling of the present invention toidentify subjects as responders or non-responders to anti-TNF drugtherapy. This example illustrates the use of disease activity profilingwith a number of patient samples from a Crohn's Disease clinical trial#1.

In particular, an array of markers was measured at various time pointsduring treatment with Remicade (infliximab; IFX) only or a treatment ofRemicade with MTX: Remicade (inflixamab), antibodies to infliximab(ATI), and neutralizing antibodies to IFX. This example shows that adisease activity profile can show the relationship among ATI, IFX andneutralizing antibodies. Analysis of clinical study #1 is illustratedherein.

FIG. 8A-B illustrates the correlation between Crohn's Disease ActivityIndex (CDAI) score and the concentration of infliximab in serum in anumber of patients in clinical study #1. In brief, 894 samples wereanalyzed. An IFX concentration≥0.1 μg/ml at the limit of detection (LOD)was defined to be “present”. The results showed that IFX negative (IFX−)samples also have significantly higher CDAI (p=0.0254, calculated byMann-Whitney U test), compared to IFX positive samples (IFX+).

Further analysis revealed that the presence of ATI correlates with lowerIFX concentrations. It was assumed that total ATI below the level ofquantitation (BLOQ) of 3.13 U/ml was set as 0 and IFX concentrationbelow the level of detection (BLOD) was set at 0. It was determined that24% of the patients (62/258) in the study were ATI+, as defined aspositive total ATI levels at one of three time points. The analysis of894 samples showed a correlation between IFX concentration and ATIlevels. In particular, the median IFX was 0 μg/ml for ATI+ samples and7.95 μg/ml for ATI− samples (p<2.2×10⁻¹⁶ by Mann-Whitney U test). FIG.9A illustrates the association between IFX concentration and thepresence of antidrug antibodies to inflixamab in samples analyzed.

Analysis shows that a high concentration of ATI in samples correlateswith the presence of neutralizing antibodies that target TNF-αbiologics. In some embodiments, assays can be used to detectneutralizing antibodies. Neutralizing antibodies were detected inpatient samples with the highest concentrations of ATI. FIG. 9Billustrates that a high concentration of ATI can lead to the presence ofneutralizing antibodies and undetectable levels of IFX.

Longitudinal analysis of the relationship of CDAI and the presence ofATI was evaluated in samples collected at clinic visit #1 and #3 from283 patients. A correlation between the presence of ATI at visit #1 (V1)was established with CDAI at visit #3 (V3). The median CDAI was 109 atV1 in ATI+ samples, while the median CDAI was 78 in ATI− samples(p=0.027 by Mann Whitney U test). The results indicate a causalrelationship between ATI positivity and CDAI. FIG. 9C illustrates thatATI+ samples determined at an early time point were more likely to havea higher CDAI at a later time. The results indicate that diseaseactivity profiling at an early time point can predict CDAI at a latertime point. FIG. 9D illustrates that in Clinical Study #1, patients hadlower odds of developing ATI if receiving a combination therapy ofinfliximab (IFX) and an immunosuppressant agent (e.g., MTX and AZA). Theodds ratio was 0.320 (p=0.0009 by Fisher's Exact test). In thisanalysis, ATI positivity (ATI+) was defined as total ATI≥3.13 U/ml.

Example 7. Disease Activity Profiling for TNF-α Mediated DiseasePrognosis Using Clinical Study #2 Samples

A. Clinical Study #2A

This example illustrates the use of a method for monitoring therapeuticefficacy in patients receiving Remicade (inflixamab) alone or incombination with an immunosuppressant agent (e.g., methotrexate,azathioprine and/or corticosteroids). This example describes usingmethods of the prevent invention to determine the disease activityprofiles of samples from a series of clinical trials.

In the analysis, we investigated the relationship between antidrugantibodies to inflixamab (ATI) and IFX concentrations in the cohort. Itwas determined that 90.6% of the patients were ATI+ (58/64), when ATI+samples were defined to be those with total ATI>3.13 U/ml at least onetime point. The median concentration of IFX in ATI positive samples was0 μg/ml and 3.74 μg/ml in ATI negative samples (P<2.2 10−¹⁶ by MannWhitney U Test). The concentration of neutralizing antibodies was 0 inATI+ samples. The results suggest that the presence of ATI reduces IFXconcentration in a patient on IFX therapy. The range of IFXconcentration for ATI− samples was 0.0-67.28 μg/ml. In ATI+ samples theIFX concentration was 0.0-26.15 μg/ml. In ATI+ samples with neutralizingantibodies (Nab) the IFX concentration ranged from 0-1.07 μg/ml. FIG.10A shows that correlation between IFX concentration and the presence ofATI in samples of clinical study #2A. The results also demonstrated thatthe odds of being ATI positive versus ATI negative are significantlyless for samples treated with an immunosuppressant agent (ISA, e.g.,methotrexate, azathioprine, corticosteroids, and combinations thereof).In this analysis 814 samples were evaluated. The odds of being ATI+ wassignificantly less for ISA-treated samples than of being ATI− (oddratio=0.564; p<0.00001 by Fisher's Exact Test). In addition, fewer ISAtreated samples expressed neutralizing ATIs. Of the 34 ATI+ samples withneutralizing antibodies analyzed, 9 of the 34 samples were ISA-treatedand 25 samples were non-ISA treated samples. This indicates that ISAtherapy can reduce the progression to ATI, and even neutralizingantibodies to IFX. FIG. 10B illustrates the relationship between ISAtherapy and the presence of ATI in the study.

Next, we investigated the relationship between ATI and inflammatorymarkers. As described herein, total ATI BLOQ was set at 0. CRPconcentration was determined by methods such as a CEER assay. Theresults show that the median concentration of CRP was lowest (5.0 μg/ml)in ATI− samples and higher (10.0 μg/ml) in ATI+ samples. Sampleexpressing neutralizing ATI had a yet higher median concentration of CRP(10.0 μg/ml). All pair-wise comparisons between CRP concentrations andATI status should that the values were significantly different (p<0.0001by Mann Whitney U tests). FIG. 10C illustrates the relationship betweenCRP concentrations and the presence of ATI (ATI and/or neutralizingATI).

We also investigated the relationship between ATI and loss of responseto therapy. In the cohort, samples were marked as having a “response”,“loss of response” and “no information” regarding IFX therapy. Thesamples were further categorized as being “True” if having a loss ofresponse or “False” if not having a loss of response. In total 777samples were analyzed. The results showed that in samples marked as“True”, there was a significantly higher odds ratio of also being ATIpositive (odds ratio=2.254, p<0.0001 by Fisher's Exact Test).Surprisingly, more samples that were positive for neutralizingantibodies to IFX were determined to be responsive to IFX, as comparedto being no longer responsive. Of 34 neutralizing ATI+ samples, 21 weremarked as “response” and 8 were marked as “loss of response”. FIG. 10Dillustrates the relationship between loss of responsiveness to IFXtherapy and the presence of ATI in the study. FIG. 11 illustrates thatlevels of ATI and neutralizing antibodies can be determined over time ina series of samples from various patients

We compared the concentration of IFX to the presence of the inflammatorymarker CRP. We defined “IFX presence” per sample as “True” if IFXwas >=0.1 μg/ml which is the LOD of the assay. The results suggest thatthe median CRP concentration was not different between samples with IFXpresent or without IFX present. The median CRP level was 7.40 μg/ml insamples with IFX, while median CRP=7.55 μg/ml in samples with IFX absent(p=0.591 by Mann Whitney U Test). FIG. 12A illustrates the comparison ofCRP levels to the presence of IFX.

We also compared the relationship between infusion reaction to thepresence of ATI. The analysis included a total of 797 samples; 30samples were categorized as having infusion reaction (“Yes”) and 767samples were categorized as having no infusion reaction (“No”). 29samples that had an infusion reaction were also ATI+ (odds ratio=35.54,p<0.0001 by Fisher's Exact Test). FIG. 12B illustrates the relationshipbetween the presence of ATI and the infusion reaction. Patientsexpressing ATI were more likely to have had an infusion reaction. Yet,for the 27 samples with neutralizing ATI, no infusion reaction wasobserved in 22 samples. The remaining 5 samples with neutralizing ATIhad infusion reaction.

B. Clinical Study #2B

In this analysis of clinical study #2B, we investigated therelationships between the presence of ATI, IFX concentration,administration of ISA, the expression of inflammatory markers (e.g.,CRP), and loss of response to IFX treatment. We determined that themedian IFX concentration was higher in samples expressing ATI comparedto those not expressing the antidrug antibodies. 15.2% of the patients(16 out of 105) were ATI+ with a total ATI>3.13 U/ml at least one timepoint. Of the 489 samples analyzed, the median IFX concentrations were0.59 μg/ml in ATI+ samples and 7.78 μg/ml in ATI− samples (p<2.2×10¹⁶ byMann Whitney U Test). FIG. 12C illustrates the relationship between IFXconcentration and the presence of ATI in the cohort. The analysis showedthat there are high odds of developing antibodies to IFX whenimmunosuppressants have been withdrawn (odds ratio=0.412, p=0.0367 byFisher's Exact Test). FIG. 12D illustrates the correlation between thepresence of ATI and the withdrawal of ISA therapy at a specific, givendate. We determined that ATI positive samples have a higher medianconcentration of CRP (9.6 μg/ml, p=1.25×10⁻¹² by Mann Whitney U Test),compared to ATI negative samples (median CRP=1.5 μg/ml). FIG. 13Aillustrates the relationship between ATI and the inflammatory markerCRP. Our analysis showed that the odds of experiencing a loss ofresponse to IFX was higher in patients determined to be ATI positive atany time point. (odds ratio=3.967, p=0.0374 for Fisher's Exact Test).FIG. 13B illustrates the correlation between the presence of ATI at anytime point and responsiveness to IFX treatment. Loss of response to IFXwas also correlated to a higher median concentration of the inflammatorymarker CRP. In the analysis there were 14 samples with loss of responseat follow-up and 91 samples from responders. The median CRP levels were11.767 μg/ml for those with loss of response and 2.585 μg/ml for thosewith response. Patients who had lost response to IFX had a significantlyhigher mean CRP (p=7.45×10⁻⁵ by Mann Whitney U Test). FIG. 13C showsthat loss of response can be related to an increase in CRP. CRP was alsosignificantly higher in samples lacking detectable IFX 2. Samples weredetermined to have IFX (“IFX present”) if the level of IFX was >= to 0.1μg/ml per sample (e.g., LOD of the assay). The median CRP was 1.6 μg/mlin IFX present samples and 13 μg/ml in IFX absent samples (p=3.69×10⁻⁵by Mann Whitney U Test). FIG. 13D illustrates the association betweenthe presence of IFX and CRP levels. In this study “ATI+” was defined asa sample with total ATI>3.13 U/ml at least one time point.

C. Clinical Study #2C

In this analysis of clinical study #2C, we investigated the relationshipbetween IFX levels and the presence of ATI. It was determined that ATI+have a significantly lower median IFX of 0.43 μg/ml as compared to ATI−samples which have a median IFX of 3.28 μg/ml (p=1.95×10⁻⁴ by MannWhitney U test). FIG. 14A shows that lower IFX levels are associatedwith the presence of ATI.

As such, in one embodiment, the present invention provides a method fordetermining whether an individual is a candidate for combination therapywherein said individual is administered infliximab, the methodcomprising:measuring for the presence or absence of ATI in saidindividual; and administering an immunosuppressant (e.g., MTX) is theindividual has significant levels of ATI. In certain aspects, theconcentration level of CRP is indicative of the presence of ATI.

Example 8. Disease Activity Profiling for TNF-α Mediated DiseasePrognosis Using Patient Samples from Clinical Study #3

This example illustrates using methods of the present invention tomonitor the therapeutic efficacy of anti-TNF drug therapy. Inparticular, pooled data including study data, pharmacokinetics data,follow-up study data of clinical study #3 were analyzed. The resultsshowed that the median IFX concentration of 0.0 μg/ml was lower in ATIpositive samples compared to an IFX concentration of 12.21 μg/ml ATInegative samples (P<2.2×10-16 by Mann Whitney U test). FIG. 14B showsthat lower IFX levels are associated with the presence of ATI in theseclinical samples. FIG. 14C illustrates that the same correlation betweenIFX levels and ATI was also present in the study data, follow-up studyand in the pharmacokinetics study (p<0.05 by Mann Whitney U tests). Wealso used methods of the present invention to determine that a highconcentration of ATI in a sample have a neutralizing effect on IFX. Inparticular, high concentrations of ATI act as neutralizing antibodies toinflixamab. Samples with a high concentration of ATI had an IFX level of0 μg/ml. FIG. 15A illustrates the relationship between ATI levelsincluding neutralizing ATI and IFX.

Example 9. Methods of Disease Activity Profiling Including the PROInflammatory Index in Patients Receiving Humira

This example illustrates methods of the present invention includingdetermining the level of TNF-α biologic (e.g., adalimumab (Humira); ADL)and the presence of anti-drug antibodies to the TNF-α biologic (e.g.,ATA) in a patient sample. In this analysis, one sample represents onepatient and a total of 98 CD samples were evaluated. 2.04% (2 out of 98CD patients) of the samples were positive for ATA, when ATA positivitywas set as total ATA>0, Surprisingly, the two ATA positive samples alsohad the highest concentrations of ADL. FIG. 15B illustrates anassociation between ADL concentration and the presence of ATA in patientsamples.

This example describes an exemplary PRO Inflammatory Index (PII). Theexample also illustrates the use of the PII in patient samples receivingHumira (adalimumab) and different drug combinations. FIG. 16A describesthe details of an exemplary PRO Inflammatory Index. The PII canrepresent a single per-sample score describing inflammation levels basedon five biomarkers. The score is obtained from the logarithmictransformation of the sum of the five biomarkers. In some embodiments,the biomarkers include VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAMin ng/ml and VCAM in ng/ml. FIG. 16B illustrates that there is noobvious relationship between the PII and the concentration of ADL in anarray of samples with ADL alone or in combination with other drugs. Thiscould be due to the appearance of high ADL trough serum concentration inthe sample cohort. These is a significant negative correlation betweenPII and ADL concentration (p=1.66×10⁻⁵ and Spearman's Rho=−0.459). Asimilar negative correlation relationship was found between IFX and PII.

We also compared the relationship between the PHI and the presence oftherapeutic agents used to treat TNF-α mediated diseases. ADL positivesamples were defined as samples with an ADL concentration of greaterthan 0 μg/ml. The results showed that a higher PII was detected inpatients on Humira compared to patients on Remicade and Humira. FIG. 17shows a plot of the PII scores for patients receiving Humira and Humirain combination with other drug such as Remicade, Cimzia, Asathioprineand Methotrexate.

As such, in one embodiment, the present invention provides a method formonitoring Crohn's disease activity, the method comprising:

determining an inflammatory index comprising the measurement of a panelof markers comprising VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM inng/ml and VCAM in ng/ml;

comparing the index to an efficacy scale or index to monitor and managethe disease.

Example 10. Methods for Improved Patient Management

This example describes methods for improved patient management to assistin developing personalized patient treatment.

In some embodiments, patients with active CD and UC can be analyzedusing a mobility shift assay (see, e.g., PCT Publication No. WO2011/056590, the disclosure of which is hereby incorporated by referencein its entirety for all purposes) in conjunction with disease activityprofiling. FIG. 18 shows details of the methods of the present inventionfor improving the management of patients with CD and/or UC. In someembodiments, the methods of disease activity profiling comprisepharmacokinetics, and determining the presence and/or levels of diseaseactivity profile markers and/or mucosal healing markers.

In some embodiments, disease activity profiling comprises methods ofdetecting, measuring, and determining the presence and/or levels ofbiomarkers, cytokines, and/or growth factors. Non-limiting examples ofcytokines that can be used in disease activity profiling include bFGF,TNF-α, IL-10, IL-12p70, IL-1β, IL-2, IL-6, GM-CSF, IL-13, IFN-γ, TGF-β1,TGF-β2, TGF-β3, and combinations thereof. Non-limiting examples ofinflammatory markers include SAA, CRP, ICAM, VCAM, and combinationsthereof. Non-limiting examples of anti-inflammatory markers includeTGF-β3, IL-10, and combinations thereof. Non-limiting examples of growthfactors include amphiregulin (AREG), epiregulin (EREG), heparin bindingepidermal growth factor (HB-EGF), hepatocye growth factor (HGF),heregulin-β1 (HRG) and isoforms, neuregulins (NRG1, NRG2, NRG3, NRG4),betacellulin (BTC), epidermal growth factor (EGF), insulin growthfactor-1 (IGF-1), transforming growth factor (TGF), platelet-derivedgrowth factor (PDGF), vascular endothelial growth factor (VEGF), stemcell factor (SCF), platelet derived growth factor (PDGF), solublefms-like tyrosine kinase 1 (sFlt1), placenta growth factor (PIGF),fibroblast growth factors (FGFs), and combinations thereof.

In other embodiments, disease activity profiling comprises detecting,measuring and determining pharmacokinetics and mucosal healing. In someaspects, mucosal healing can be assessed by the presence and/or level ofselected biomarkers and/or endoscopy. In some instances, mucosal healingcan be defined as the absence of friability, blood, erosions and ulcersin all visualized segments of gut mucosa. In some embodiments,biomarkers of mucosal healing, include, but are not limited to, AREG,EREG, HG-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF-1, HRG, FGF1,FGF2 (bFGF), FGF7, FGF9, SCF, PDGF, TWEAK, GM-CSF, TNF-α, IL-12p70,IL-1β, Il-2, IL-6, IL-10, IL-13, IFN-γ, TGF-α, TGF-β1, TGF-β2, TGF-β3,SAA, CRP, ICAM, VCAM, and combinations thereof. In some embodiments, agrowth factor index can be established using statistical analyses of thedetected levels of biomarkers of mucosal healing. In some instances, thegrowth factor index can be associated with other markers of diseaseactivity, and utilized in methods of the present invention topersonalize patient treatment.

FIG. 19 shows the effect of the TNF-α pathway and related pathways ondifferent cell types, cellular mechanisms and disease (e.g., Crohn'sDisease (CD), rheumatoid arthritis (RA) and Psoriasis (Ps)). FIG. 20illustrates a schematic of an exemplary CEER multiplex growth factorarray. In particular embodiments, the methods of the present inventioncan employ this array. As non-limiting examples, FIG. 21A-F illustratemultiplexed growth factor profiling of patient samples using this array.In particular, longitudinal analysis of growth factors, such as AREG,EREG, HB-EGF, HGF, HRG. BTC, EGF, IGF, TGFα, and VEGF, was performed ona collection of patient samples. FIGS. 21B and E illustrate thedetermination of the level of serological and immune markers, such asASCA-a, ASCA-g, Cbir1 and OmpC, in samples from Patient 10109, Patient10118 and Patient 10308. FIG. 21G shows the exemplary growth factorarrays performed on samples from healthy controls, patients with IBS-C,and patients with IBS-D.

A series of multiplexed CEER growth factor and CRP arrays was performedon patient samples. Tables A-D (below) highlight longitudinal analysisof mucosal healing in patient samples. The following Table (A) showsthat CRP and growth factors can be predictive of mucosal healing:

Subject Collection TGF TGF ID Date CRP EGF bFGF VEGF FGF1 Tweak beta1beta2 10101 Collection 1 3.98 315.67 4.83 1454.94 15.16 0.65 68.64964.02 10101 Collection 2 0.13 N 365.74 P 3.79 N 1201.53 N 15.37 P 6.39P 78.52 P 562.77 N 10103 Collection 1 0.66 439.03 4.00 969.78 17.8635.05 67.68 300.36 10103 Collection 2 15.44 P 372.64 N 3.90 N 881.27 N17.00 N 35.50 P 73.85 P 311.76 P 10109 Collection 1 15.86 418.89 1.66223.85 13.52 6.35 63.79 386.64 10109 Collection 2 1.22 N 162.75 N 0.49 N177.42 N 15.66 P 5.69 N 57.93 N 544.34 P 10118 Collection 1 0.41 126.861.31 1173.42 13.11 9.51 71.43 339.43 10118 Collection 2 3.54 P 282.16 P3.03 P 1200.74 P 14.43 P 1.92 N 68.69 N 920.00 P 10308 Collection 1 1.80336.45 2.23 1361.03 15.05 5.35 98.94 730.52 10308 Collection 2 155.95 P525.57 P 23.83 P 3233.27 P 15.34 P 11.18 P 153.21 P 466.49 N 10503Collection 1 2.10 237.62 6.76 760.17 13.63 13.07 64.72 475.00 10503Collection 2 27.39 P 215.81 N 3.59 N 1135.46 P 11.81 N 61.50 P 90.11 P737.82 P 11003 Collection 1 6.32 1.58 408.49 14.06 7.69 40.53 395.1911003 Collection 2 0.16 N 123.57 1.90 P 394.88 N 14.81 P 5.12 N 35.85 N221.67 N 11601 Collection 1 0.23 241.76 3.36 173.02 15.43 2.54 46.92589.91 11601 Collection 2 0.92 P 310.64 P 6.89 P 169.40 N 17.31 P 12.50P 61.67 P 514.21 N 11602 Collection 1 1.71 327.92 15.31 562.30 12.8212.13 58.15 1120.06 11602 Collection 2 1.93 P 338.88 P 4.83 N 334.69 N12.36 N 14.61 P 59.83 P 1599.43 P 12121 Collection 1 6.85 484.22 4.89477.49 11.90 25.90 35.44 1307.92 12121 Collection 2 2.16 N 607.95 P 4.72N 842.54 P 11.13 N 10.93 N 43.32 P 1284.24 N 12121 Collection 3 58.64 P458.80 N 0.81 N 286.72 N 12.38 P 6.71 N 60.23 P 631.34 N 190 Collection1 0.74 353.47 252.71 1.63 22.79 190 Collection 2 25.18 P 941.21 P 656.11P 4.71 P 84.07 P 492 Collection 1 0.66 351.79 3.61 20.02 492 Collection2 91.49 P 962.96 P 27.89 P 2546 Collection 1 4.73 857.25 866.87 10.3731.01 2546 Collection 2 28.18 P 805.11 N 826.44 N 7.23 N 56.28 P “N” and“P” denote a negative or positive relationship between pairs ofobservations for each marker, respectively per subject. Underlined dataare number pairs above upper limit of quantitation and are assumed tohave a positive relationship.

The following Table B lists CRP and growth factors predictive of mucosalhealing:

Subject Collection HB TGF ID Date CRP AREG HGF HRG EGF BTC alpha 10101Collection 1 3.98 12.16 26.60 143.10 6.60 0.00 3.10 10101 Collection 20.13 N 9.91 N 23.90 N  46.50 N 5.00 N 0.00 2.00 N 10103 Collection 10.66 26.06 41.30 2000.00  17.80 0.00 6.60 10103 Collection 2 15.44 P50.00 P 82.70 P 2000.00  P 18.10 P 0.00 6.60 P 10109 Collection 1 15.8613.81 27.40 243.10 7.80 0.00 3.30 10109 Collection 2 1.22 N 9.71 N 0.00N  95.60 N 6.30 N 0.00 2.60 N 10118 Collection 1 0.41 27.10 26.12 541.3010.51 0.00 7.07 10118 Collection 2 3.54 P 21.40 N 29.95 P 492.70 P 7.83N 26.00 P 6.42 P 10308 Collection 1 1.80 0.00 45.10  0.00 4.28 0.00 0.0010308 Collection 2 155.95 P 0.00 121.67 P  0.00 5.31 P 0.00 0.00 10503Collection 1 2.10 4.90 35.36  0.00 2.80 0.00 0.00 10503 Collection 227.39 P 7.10 P 46.07 P 145.70 P 5.80 P 0.00 1.70 P 11003 Collection 16.32 10.20 31.11 205.20 5.80 0.00 3.30 11003 Collection 2 0.16 N 7.30 N25.98 N 124.50 N 5.40 N 0.00 2.80 N 11601 Collection 1 0.23 0.00 8.10 0.00 8.40 8.00 1.39 11601 Collection 2 0.92 P 6.00 P 12.90 P 467.00 P8.80 P 8.30 P 1.91 P 11602 Collection 1 1.71 0.00 55.50  0.00 7.50 7.400.70 11602 Collection 2 1.93 P 0.00 11.90 N  0.00 5.40 N 7.70 P 0.88 P12121 Collection 1 6.85 0.00 37.40  0.00 7.80 9.00 2.63 12121 Collection2 2.16 N 0.00 54.00 P  0.00 8.00 P 7.40 N 0.24 N 12121 Collection 358.64 P 0.00 66.30 P  0.00 7.90 P 7.50 N 1.86 N 190 Collection 1 0.740.00 10.80  0.00 5.90 0.00 1.90 190 Collection 2 25.18 P 0.00 11.50 P 0.00 5.70 N 0.00 0.00 N 492 Collection 1 0.66 10.90 13.00 441.80 19.400.00 3.11 492 Collection 2 91.49 P 8.60 N 21.00 P 355.60 P 13.30 N 0.002.12 N 2546 Collection 1 4.73 11.55 16.70 299.40 8.00 0.00 3.50 2546Collection 2 28.18 P 26.12 P 38.80 P 912.70 P 13.30 P 0.00 5.40 P “N”and “P” denote a negative or positive relationship between pairs ofobservations for each marker, respectively per subject. Underlined dataare number pairs above upper limit of quantitation and are assumed tohave a positive relationship.

The following Table C shows that CRP and growth factors can bepredictive of mucosal healing:

Subject Collection TGF ID Date CRP EGF VEGF Tweak beta1 2834 Collection1 6.88 604.22 624.03 2.00 68.05 2834 Collection 2 24.33 P 631.31 P509.73 N 3.72 P 44.79 N 3570 Collection 1 105.46 1046.04 191.49 5.5133.61 3570 Collection 2 1.31 N 487.25 N 237.91 P 6.33 P 41.29 P 3713Collection 1 7.76 1117.85 1267.74 3.94 45.08 3713 Collection 2 107.22 P633.56 N 957.18 N 5.44 P 39.59 N 5301 Collection 1 7.62 32.19 5301Collection 2 36.61 P 217.02 389.33 2.88 30.89 N 7757 Collection 1 4.49838.39 11.24 7.90 43.35 7757 Collection 2 138.56 P 705.18 N 5.33 N 7966Collection 1 3.03 120.82 326.72 5.59 38.67 7966 Collection 2 31.04 P1089.52 P 691.29 P 6.81 P 48.68 P 8075 Collection 1 6.81 968.26 840.068.10 58.65 8075 Collection 2 34.62 P 620.97 N 876.55 P 6.27 N 51.36 N8127 Collection 1 34.41 323.51 310.67 5.54 41.13 8127 Collection 2 2.78N 318.02 N 284.46 N 6.87 P 51.87 P 8431 Collection 1 4.53 1829.91 214.782.18 52.82 8431 Collection 2 30.51 P 816.10 N 301.14 P 3.47 P 58.41 P3831 Collection 1 32.95 804.87 491.46 6.83 36.16 3831 Collection 2 0.29N 491.17 N 912.29 P 7.31 P 23.62 N 3852 Collection 1 68.59 494.06 252.186.10 32.76 3852 Collection 2 1.00 N 291.49 N 122.66 N 6.56 P 39.22 P3852 Collection 3 0.60 N 375.97 N 100.53 N 1.34 N 22.83 N 5477Collection 1 23.17 550.58 485.76 7.51 36.73 5477 Collection 2 2.12 N1101.83 P 575.69 P 7.55 P 34.98 N 7456 Collection 1 35.21 51.23 452.456.13 22.05 7456 Collection 2 0.89 N 496.87 P 366.73 N 0.99 N 14.19 N “N”and “P” denote a negative or positive relationship between pairs ofobservations for each marker, respectively per subject. Underlined dataare number pairs above upper limit of quantitation and are assumed tohave a positive relationship.

The following Table D shows that CRP and growth factors can bepredictive of mucosal healing:

Subject Collection HB TGF ID Date CRP AREG HGF HRG EGF BTC alpha 2834Collection 1 6.88 2834 Collection 2 24.33 P 3570 Collection 1 105.463570 Collection 2 1.31 N 3713 Collection 1 7.76 0.00 17.40 0.00 6.900.00 2.20 3713 Collection 2 107.22 P 0.00 13.20 N 0.00 5.80 N 0.00 0.00N 5301 Collection 1 7.62 5301 Collection 2 36.61 P 7757 Collection 14.49 2.60 26.00 0.00 6.90 0.00 6.82 7757 Collection 2 138.56 P 2.70 P43.00 P 0.00 5.70 N 0.00 6.82 P 7966 Collection 1 3.03 4.20 21.00 0.006.10 34.58 3.33 7966 Collection 2 31.04 P 2.40 N 36.00 P 0.00 5.70 N0.00 N 2.62 N 8075 Collection 1 6.81 8.50 14.70 359.30 8.90 0.00 0.008075 Collection 2 34.62 P 6.40 N 16.80 P 0.00 N 6.30 N 0.00 0.00 8127Collection 1 34.41 13.20 16.50 476.30 23.00 16.90 5.28 8127 Collection 22.78 N 9.40 N 16.90 P 355.30 P 9.10 N 0.00 N 3.46 N 8431 Collection 14.53 5.00 21.40 0.00 5.50 0.00 0.00 8431 Collection 2 30.51 P 31.30 P24.80 P 671.90 P 23.10 P 0.00 25.00 P 3831 Collection 1 32.95 3831Collection 2 0.29 N 3852 Collection 1 68.59 0.00 17.08 0.00 6.00 0.000.00 3852 Collection 2 1.00 N 0.00 13.84 N 0.00 5.10 N 0.00 0.00 3852Collection 3 0.60 N 5477 Collection 1 2317 2.30 15.25 143.20 5.60 0.000.00 5477 Collection 2 2.12 N 2.00 N 19.05 P 0.00 N 5.40 N 0.00 2.50 P7456 Collection 1 35.21 16.70 0.00 315.30 8.10 0.00 5.10 7456 Collection2 0.89 N 6.60 N 12.92 P 128.90 N 3.60 N 0.00 2.70 N “N” and “P” denote anegative or positive relationship between pairs of observations for eachmarker, respectively per subject. Underlined data are number pairs aboveupper limit of quantitation and are assumed to have a positiverelationship.

Tables A, B, C and D show marker values and relationships between pairsof observations in CRP and growth factor data. Using a criterion ofα=0.1, we identified an association between three growth factors andCRP. The following Table (E) shows a two-by-two contingency table thathighlights that an increase or decrease in AREG, HRG and TGF was foundto be significantly associated with an increase or decrease of CRP:

AREG* HRG** TGF-alpha*** Positive Negative Positive Negative PositiveNegative CRP Positive 6 4 7 1 8 5 Negative 0 6 1 5 1 6 *denotes p =0.034. **denotes p = 0.026. ***denotes p = 0.07.

FIG. 22 illustrates the association between CRP levels and the growthfactor index score in determining disease remission.

Further studies for identifying predictive markers of mucosal healingmay include samples from several clinical studies. As one non-limitingexample, Clinical Study A may include 413 samples (paired samples with1-5 samples per patient). Clinical data may detail patient age, sex,weight, date of diagnosis, disease location, sample collection dates,dose, colonoscopy, improvement of mucosa, presence of mucosal healing,and/or concomitant medication useage. In Clinical Study A, colonoscopymay be performed prior to first drug infusion. As another non-limitingexample, in Clinical Study B, 212 UC samples may be analyzed (110samples were diagnosed for CD at follow-up and 102 samples werediagnosed for UC based on mucosal healing). Clinical data may detailpatient age, sex, weight, date of diagnosis, disease location, samplecollection dates, IFX dose, colonoscopy results (endoscopic activityscore), albumin level, CRP level, and/or Mayo score. In Clinical StudiesA and B, three infusions may occur at week 0, 2 and 6 during induction.6 additional drug infusions may be performed during the maintenancephase at week 14, 22, 30, 38, 46 and 52. A second colonoscopy may beperformed during the maintenance phase. A third colonscopy may beperformed during follow-up and patients may continue treatment ifresponsive to drug.

The methods of the present invention can be used to create personalizedtherapeutic management of a TNFα-mediated disease. A personalizedtherapeutic regimen for a patient diagnosed with IBD can be selectedbased on predictors of disease status and/or long-term outcome asdescribed herein, including, but not limited to, a Crohn's prognostictest (see, e.g., PCT Publication No. WO 2010/120814, the disclosure ofwhich is hereby incorporated by reference in its entirety for allpurposes), a disease activity profile (e.g., disease burden), a mucosalstatus index, and/or a PRO Inflammatory Index as described in Example 5.Using the methods of the present invention, it can be determined that apatient has mild disease activity and the clinician can recommend,prescribe, and/or administer a nutrition-based therapy (FIG. 23A). Yet,if it is determined that a patient has mild disease activity with anaggressive phenotype, a nutrition-based therapy in addition tothiopurines can be recommended, prescribed, and/or administered. Asimilar therapy can be recommended, prescribed, and/or administered ifit is determined that the patient has moderate disease activity (FIG.23B). If it is determined that a patient has moderate disease activitywith an aggressive phenotype, either a combination of thiopurines andnutrition therapy (Nx) or an appropriate anti-TNF drug can berecommended, prescribed, and/or administered. In some instances, ananti-TNF monitoring test (see, e.g., PCT Publication No. WO 2011/056590,the disclosure of which is hereby incorporated by reference in itsentirety for all purposes) can be used to determine if the patient islikely to respond to the therapy. In the case when severe diseaseactivity is determined, an appropriate anti-TNF drug administered at anoptimized dose can be recommended and/or prescribed (FIG. 23C). In suchinstances, an anti-TNF monitoring test (see, e.g., PCT Publication No.WO 2011/056590, the disclosure of which is hereby incorporated byreference in its entirety for all purposes) can be used to predict ifthe patient is likely to be responsive to drug. In other instances, itcan be recommended and/or prescribed that a patient having severedisease activity also receive nutrition-based therapy.

In some embodiments, the methods of the present invention can be used ina treatment paradigm to personalize patient treatment (FIG. 24). First,treatment can be selected based on the expression of mucosal statusmarkers. Next, drug dose can be selected based on disease burden (e.g.,disease activity index). After the therapeutic drug is administered, theinitial response can be determined from the expression of markers ofmucosal healing. ATM monitoring can be used to identify patient who areresponsive or non-responsive to therapy. Non-responsive patients canthen be prescribed an appropriate anti-TNF drug.

Example 11. Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI)Assays are Predictive of Disease Activity in Patients with Crohn'sDisease (CD)

Previous studies indicate that patients with CD who have a higher troughconcentration of IFX during maintenance dosing are more likely tobenefit from treatment. However, development of ATIs can result inincreased drug clearance and loss of response. Therapeutic drugmonitoring may allow clinicians to maintain effective drugconcentrations. Although previous ATI assays have been limited by theinability to measure ATIs in the presence of drug, fluid-phase IFX andATI assays have overcome this problem (see, e.g., PCT Publication No. WO2011/056590, the disclosure of which is hereby incorporated by referencein its entirety for all purposes). We used these assays to evaluate therelationship between serum IFX concentration, ATIs and disease activity.

Methods:

2021 serum samples from 532 participants in 4 prospective CD RCTs orcohort studies (COMMIT, Leuven dose optimization study, CanadianMulticenter and IMEDEXI) that evaluated the maintenance phase of IFXtreatment were used, and data were combined for analysis. IFX and ATIserum levels were measured using a HPLC-based fluid phase assay. CRP,measured by ELISA, was used to assess disease activity. ROC analysisdetermined the IFX threshold that best discriminated disease activity,as measured by CRP. We examined pairs of samples taken over sequentialtime points and evaluated the relationship between IFX and ATI presencein the pair's first data point and CRP in the subsequent measurement.There were 1205 such observations. We identified four distinct patientgroups, namely IFX≥threshold and ATI−, IFX<threshold and ATI−,IFX≥threshold and ATI+, and IFX<threshold and ATI+. Regression analysesassessed the potential interaction between IFX and ATI as predictors ofCRP.

Results:

CRP can best differentiate IFX status with an IFX concentrationthreshold of 3 μg/ml (ROC AUC=74%). Using paired sequential samples bothATI and IFX were associated with median CRP (Table 2). Although ATI+patients had higher CRP levels overall, within this group there was noassociation between IFX higher than threshold and subsequent CRP. InATI− patients, CRP was significantly higher in patients with IFXlevels<3 μg/ml. In the regression analysis ATI positivity, IFX≥3 μg/mland the interaction term were all significant predictors of CRP. CRP was31% higher in ATI positive patients than those who were ATI negative and62% lower in patients with IFX levels≥3 μg/ml compared to those withIFX<3 μg/ml.

Conclusions:

We have shown that ATI positivity is predictive of increased diseaseactivity, while an IFX concentration above the threshold value of 3μg/ml is predictive of significantly lower disease activity. In ATI+patients, IFX concentrations above 3 μg/ml had no effect on CRP,indicating that the benefits of IFX are diminished in the presence ofATI despite the presence of optimal drug concentration. These findingssupport the concept that therapeutic drug monitoring is an importanttool in optimizing IFX therapy. Using paired sequential samples andregression analysis, both ATI and IFX were associated with median CRP asshown in the following table:

Median CRP Concentration (ng/ml; interquartile range) In ATI− PatientsIn ATI+ Patients Significance IFX < 3 μg/ml 5.65 (1.68, 16.1) 8.40(3.10, 20.1) *** IFX ≥ 3 μg/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2) **Significance *** NS Median CRP concentrations and interquartile ranges(in parentheses) in ng/ml. Asterisks denote significance levels oftwo-sample Mann-Whitney U tests (***, p < 0.001; **, p < 0.01; *, p <0.05; NS, not significant).

Example 12. Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI)Assays are Predictive of Disease Activity in Patients with Crohn'sDisease (CD)

This example illustrates the use of infliximab (IFX) andantibody-to-infliximab (ATI) assay in predicting disease activity inpatients with Crohn's disease (CD). This example also illustrates amethod of determining the threshold of IFX that can best discriminatedisease activity as measured by C-reactive protein (CRP) levels. Thisexample also illustrates the association of both ATI and IFX to CD andCRP levels, which can serve as a measure of disease activity.

Previous studies have indicated that patients with CD who have a highertrough concentration of IFX during maintenance dosing are more likely tobenefit from treatment. However, development of ATIs can result inincreased drug clearance and loss of response. Therapeutic drugmonitoring may allow clinicians to maintain effective drugconcentrations. Although previous ATI assays have been limited by theinability to measure ATIs in the presence of drug, the fluid-phase IFXand ATI assays described in PCT Publication No. WO 2011/056590 (thedisclosure of which is hereby incorporated by reference in its entiretyfor all purposes) have overcome this problem.

In this study we used fluid-phase IFX and ATI assays to evaluate therelationship between serum IFX concentration, ATIs and disease activity,as measured by CRP. We analyzed 2,021 serum samples from 532participants in 4 prospective CD randomized controlled trials (RCTs) orcohort studies, including COMMIT, Leuven dose optimization study,Canadian Multicenter and IMEDEXI. The combined analysis was restrictedto samples during maintenance of IFX treatment. There was evidence ofnon-heterogeneity among pooled CRP.

IFX and ATI serum levels were measured using a HPLC-based fluid phaseassay. CRP was measured by ELISA and used to assess disease activity.Receiver-operator curve (ROC) analysis was performed to determine theIFX trough threshold (e.g., amount or concentration) that can bestdiscriminate disease activity (e.g., between high and low CRP values).FIG. 25 shows the ROC analysis. CRP and nine IFX trough thresholds wereanalyzed and the ROC area under receiver-operator characteristic curve(AUC) are as follows:

IFX trough threshold (μg/ml) 0.1 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 ROC0.682 0.727 0.733 0.743 0.727 0.717 0.699 0.689 0.678 AUC

The ROC analysis showed that CRP can best differentiate IFX status withan IFX concentration threshold of 3 μg/ml (ROC AUC=74%). For example, atan IFX through concentration threshold of 3.0 μg/ml, a randomly chosensample with a “low” IFX serum concentration will have a higher CRP levelthan a randomly chosen sample with a “high” IFX serum concentration74.3% of the time. In the IFX, ATI and CRP association analysis, a serumIFX trough threshold of 3.0 μg/ml was used.

To determine the association of serum IFX concentration, ATI, and CRPlevels over time, we examined pairs of samples taken over sequentialtime points. A 100-day time gap limit was imposed for the time points.We evaluated the relationship between the presence of IFX and ATI in thepair's first data point and CRP in the subsequent measurements (FIG.26A). FIG. 26B shows CRP levels, IFX serum concentration and ATI statusat sequential time points for a sample. In total, 1,205 observationswere examined.

Regression analysis (e.g., ordinary least squares regression) wasperformed to assess the potential interaction between prior IFX andprior ATI as predictors of disease (i.e., CRP levels). In particular,CRP was log transformed at the second time point observation. Prior IFXis the first time point with IFX concentration above or below thecalculated trough threshold of 3 μg/ml. Prior ATI is the first timepoint ATI is above or below 3.13 U/ml which is the limit of detection(LOD). Using paired sequential samples and regression analysis, both ATIand IFX were associated with median CRP as shown in the following table:

Median CRP Concentration (ng/ml; interquartile range) In ATI− PatientsIn ATI+ Patients Significance IFX < 3 μg/ml 5.65 (1.68, 16.1) 8.40(3.10, 20.1) *** IFX ≥ 3 μg/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2) **Significance *** NS Median CRP concentrations and interquartile ranges(in parentheses) in ng/ml. Asterisks denote significance levels oftwo-sample Mann-Whitney U tests (***, p < 0.001; **, p < 0.01; *, p <0.05; NS, not significant).

The results shows that the factors and interactions between the factorsare significant. The regression coefficients were calculated to be 0.272for ATI+ samples and −0.979 for IFX≥3 μg/ml.

We identified four distinct patient groups: (1) IFX≥threshold and ATI−,(2) IFX<threshold and ATI−, (3) IFX≥threshold and ATI+, and (4)IFX<threshold and ATI+. Of the 1,205 observations used in the analysis,605 were IFX≥threshold and ATI−; 196 were IFX<threshold and ATI−; 41were IFX≥threshold and ATI+; and 363 were IFX<threshold and ATI+.

Although ATI+ patients had higher CRP levels overall, within this groupthere was no association between IFX levels higher than threshold andCRP (FIG. 27). In ATI− patients, CRP levels were significantly higher inpatients with IFX levels less than threshold (FIG. 27).

In the regression analysis, ATI positivity, IFX≥3 μg/ml and theirinteraction were all significant predictors of CRP levels. CRP was 31%higher in ATI+ patients than those who were ATI−, and 62% lower inpatients with IFX levels≥3 μg/ml compared to those with IFX<3 μg/ml. Therelationship between IFX concentration and CRP levels differs betweenATI+ and ATI− patient groups.

In this study we showed that ATI positivity is predictive of increaseddisease activity, as measured by CRP. We also showed that IFXconcentration above the threshold value of 3 μg/ml is predictive ofsignificantly lower disease activity. In ATI+ patients, IFXconcentrations above 3 μg/ml had no effect on CRP levels, suggestingthat the benefits of IFX are diminished in the presence of ATI evendespite the presence of optimal drug concentration.

We showed that disease activity, as measured by CRP, is strongly linkedto both IFX and ATI in a large combined dataset. Thus, patients withactive Crohn's disease can benefit from knowledge of both IFX and ATIlevels at trough. Based on the experimental derivation of theserelationships, the following treatment paradigms were created. Forinstance, a symptomatic patient with Crohn's disease with IFX<thresholdat trough and ATI− can benefit from an increased dose of IFX therapy. Apatient with IFX≥threshold and ATI− can benefit from receiving endoscopyor switching therapy. A symptomatic patient with IFX<threshold at troughand ATI+ can benefit from switching therapy if ATI is high or optimizingtherapy dose if ATI is low. A patient with IFX≥threshold and ATI+ canbenefit from switching therapy if disease activity (e.g., CRP level) ishigh. Alternatively, if disease activity (e.g., CRP level) is low inthat patient, further monitoring is recommended. The treatment paradigmsare described in the following table:

ATI− ATI+ IFX < threshold Increase dose Switch therapy (high ATI) orOptimize dose (low ATI) IFX ≥ threshold Check endoscopy Switch therapy(high activity) or or Switch therapy Monitor (low activity)

These findings demonstrate that therapeutic drug monitoring usingmethods of the present invention are important tools in optimizing IFXtherapy.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, one of skill in the art will appreciate that certainchanges and modifications may be practiced within the scope of theappended claims. In addition, each reference provided herein isincorporated by reference in its entirety to the same extent as if eachreference was individually incorporated by reference.

What is claimed is:
 1. A method for monitoring therapeutic efficiency inan individual receiving therapy having inflammatory bowel disease (IBD),wherein the IBD comprises Crohn's disease, the method comprising: (a)measuring the levels of an array of mucosal healing markers in a samplefrom the individual at a plurality of time points over the course oftherapy; (b) applying a statistical algorithm to the level of the one ormore markers determined in step (a) to generate a mucosal healing index;(c) comparing the individual's mucosal healing index to that of acontrol, wherein the control is an endoscopic score; (d) determiningwhether the therapy is appropriate for the individual to promote mucosalhealing; and (e) administering an appropriate therapy.
 2. The method ofclaim 1, wherein the mucosal healing marker is a member selected fromthe group consisting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4,BTC, EGF, IGF, TGF-α, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7,FGF9, TWEAK and combinations thereof.
 3. The method of claim 1, whereinthe markers are measured in a sample selected from the group consistingof serum, plasma, whole blood, stool, peripheral blood mononuclear cells(PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy.
 4. Themethod of claim 1, wherein the appropriate therapy is selected from thegroup consisting of TNFα inhibitor therapy, an immunosuppressive agent,a corticosteroid, a drug that targets a different mechanism, nutritiontherapy, and combinations thereof.
 5. The method of claim 4, wherein theTNFα inhibitor therapy comprises an anti-TNFα antibody.
 6. The method ofclaim 5, wherein the anti-TNFα antibody is a member selected from thegroup consisting of REMICADE™ (infliximab), ENBREL™ (etanercept),HUMIRA™ (adalimumab), CIMZIA® (certolizumab pegol), and combinationsthereof.
 7. The method of claim 4, wherein the immunosuppressive agentis a member selected from the group consisting of azathioprine,6-mercaptopurine, methotrexate, and combinations thereof.
 8. The methodof claim 4, wherein the drug that targets a different mechanism is amember selected from the group consisting of an IL-6 receptor inhibitingantibody, an anti-integrin molecule, a JAK-2 inhibitor, a tyrosinekinase inhibitor, and combinations thereof.
 9. The method of claim 4,wherein the nutrition therapy comprises a special carbohydrate diet. 10.The method of claim 1, wherein the array of mucosal healing markersfurther comprises at least one member selected from the group consistingof an anti-TNFα antibody, an anti-drug antibody (ADA), an inflammatorymarker, an anti-inflammatory marker, a mucosal healing marker, andcombinations thereof.
 11. The method of claim 10, wherein the anti-TNFαantibody is a member selected from the group consisting of REMICADE™(infliximab), ENBREL™ (etanercept), HUMIRA™ (adalimumab), CIMZIA®(certolizumab pegol), and combinations thereof.
 12. The method of claim10, wherein the anti-drug antibody (ADA) is a member selected from thegroup consisting of a human anti-chimeric antibody (HACA), a humananti-humanized antibody (HAHA), a human anti-mouse antibody (HAMA), andcombinations thereof.
 13. The method of claim 10, wherein the mucosalhealing marker is a member selected from the group consisting of AREG,EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-α, VEGF-A,VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, TWEAK and combinationsthereof.
 14. The method of claim 10, wherein the inflammatory marker isa member selected from the group consisting of GM-CSF, IFN-γ, IL-1β,IL-2, IL-6, IL-8, TNF-α, sTNF RII, and combinations thereof.
 15. Themethod of claim 10, wherein the anti-inflammatory marker is a memberselected from the group consisting of IL-12p70, IL-10, and combinationsthereof.
 16. The method of claim 1, wherein: (i) the marker is acytokine selected from the group consisting of GM-CSF, IFN-γ, IL-1β,IL-2, IL-6, IL-8, TNF-α, soluble tumor necrosis factor-α receptor II(sTNF RII), TNF-related weak inducer of apoptosis (TWEAK),osteoprotegerin (OPG), IFN-α, IFN-β, IL-1α, IL-1 receptor antagonist(IL-1ra), IL-4, IL-5, soluble IL-6 receptor (sIL-6R), IL-7, IL-9, IL-12,IL-13, IL-15, IL-17, IL-23, and IL-27; or (ii) the marker is selectedfrom the group consisting of MMP-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9,MMP-12, MMP-13, MT1-MMP-1, and combinations thereof; or (iii) the markeris selected from the group consisting of C-reactive protein (CRP),D-dimer protein, mannose-binding protein, alpha 1-antitrypsin, alpha1-antichymotrypsin, alpha 2-macroglobulin, fibrinogen, prothrombin,factor VIII, von Willebrand factor, plasminogen, complement factors,ferritin, serum amyloid P component, serum amyloid A (SAA), orosomucoid(alpha 1-acid glycoprotein (AGP)), ceruloplasmin, haptoglobin, andcombinations thereof; or (iv) the marker is selected from the groupconsisting of TGF-α, TGF-β, TGF-β2, and TGF-β3; or (v) the markerselected from the group consisting of AREG, EREG, HB-EGF, HGF, HRG,NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF-1, TGF, VEGF-A, VEGF-B, VEGF-C,VEGF-D, FGF1, FGF2, FGF7, FGF9, and TWEAK; or (vi) the marker selectedfrom the group consisting of IL-10, SCF, ICAM, VCAM, IL-12p40, andVEGFA.
 17. The method of claim 1, wherein the array of markers aremembers selected from the group consisting of C-reactive protein (CRP),IL 7, MMP 1, MMP 2, MMP 3, MMP 9, serum amyloid A (SAA), TGFα, VCAM anda combination thereof.
 18. A method for reducing or minimizing the riskof surgery in an individual being administered a therapy regimendiagnosed with Crohn's disease, said method comprising: (a) measuring anarray of mucosal healing markers at a plurality of time points over thecourse of therapy with a therapeutic antibody; (b) generating theindividual's mucosal healing index comprising a representation of thepresence and/or concentration levels of each of the markers over time;(c) comparing the individual's mucosal healing index to that of acontrol, wherein the control is an endoscopic score; (d) selecting anappropriate therapy regimen to reduce or minimize the risk of surgery;and (e) administering an appropriate therapy regimen.
 19. The method ofclaim 18, wherein: (i) the marker is a cytokine selected from the groupconsisting of GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, solubletumor necrosis factor-α receptor II (sTNF RII), TNF-related weak inducerof apoptosis (TWEAK), osteoprotegerin (OPG), IFN-α, IFN-β, IL-1α, IL-1receptor antagonist (IL-1ra), IL-4, IL-5, soluble IL-6 receptor(sIL-6R), IL-7, IL-9, IL-12, IL-13, IL-15, IL-17, IL-23, and IL-27; or(ii) the marker is selected from the group consisting of MMP-1, MMP-2,MMP-3, MMP-7, MMP-8, MMP-9, MMP-12, MMP-13, MT1-MMP-1, and combinationsthereof; or (iii) the marker is selected from the group consisting ofC-reactive protein (CRP), D-dimer protein, mannose-binding protein,alpha 1-antitrypsin, alpha 1-antichymotrypsin, alpha 2-macroglobulin,fibrinogen, prothrombin, factor VIII, von Willebrand factor,plasminogen, complement factors, ferritin, serum amyloid P component,serum amyloid A (SAA), orosomucoid (alpha 1-acid glycoprotein (AGP)),ceruloplasmin, haptoglobin, and combinations thereof; or (iv) the markeris selected from the group consisting of TGF-α, TGF-β, TGF-β2, andTGF-β3; or (v) the marker selected from the group consisting of AREG,EREG, HB-EGF, HGF, HRG, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF-1, TGF,VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, and TWEAK; or(vi) the marker selected from the group consisting of IL-10, SCF, ICAM,VCAM, IL-12p40, and VEGFA.
 20. The method of claim 18, wherein the arrayof markers are members selected from the group consisting of C-reactiveprotein (CRP), IL 7, MMP 1, MMP 2, MMP 3, MMP 9, serum amyloid A (SAA),TGFα, VCAM and a combination thereof.
 21. The method of claim 18,wherein the appropriate therapy regimen is selected from the groupconsisting of TNFα inhibitor therapy, an immunosuppressive agent, acorticosteroid, a drug that targets a different mechanism, nutritiontherapy, and combinations thereof.